B
PROBABILITY AND RANDOM PROCESSES
WILEY SURVIVAL GUIDES IN ENGINEERING AND SCIENCE Emmanuel Desurvire, Editor Wiley Survival Guide in Global Telecommunications: Signaling Principles, Network Protocols, and Wireless Systems Emmanuel Desurvire Wiley Survival Guide in Global Telecommunications: Broadband Access, Optical Components and Networks, and Cryptography Emmanuel Desurvire
Fiber to the Home: The New Empowerment Paul E. Green, Jr. Probability and Random Processes Venkatarama Krishnan
B PROBABILITY AND RANDOM PROCESSES
Venkatarama Krishnan Professor Emeritus of Electrical Engineering University of Massachusetts Lowell
A John Wiley & Sons, Inc., Publication
Copyright # 2006 by John Wiley & Sons, Inc. All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Krishnan, Venkatarama, Probability and random processes / by Venkatarama Krishnan. p. cm. — (Wiley survival guides in engineering and science) Includes bibliographical references and index. ISBN-13: 978-0-471-70354-9 (acid-free paper) ISBN-10: 0-471-70354-0 (acid-free paper) 1. Probabilities. 2. Stochastic processes. 3. Engineering—Statistical methods. 4. Science—Statistical methods. I. Title. II. Series QA273.K74 2006 519.2–dc22
2005057726
Printed in the United States of America 10 9 8 7
6 5 4 3
2 1
Contents
Preface, xi
4.6 Hypergeometric Distribution, 46 4.7 Poisson Distribution, 48
CHAPTER 1 Sets, Fields, and Events, 1 1.1
Set Definitions, 1
1.2
Set Operations, 3
1.3
Set Algebras, Fields, and Events, 8
4.8 Logarithmic Distribution, 55 4.9 Summary of Discrete Distributions, 62
CHAPTER 5 Random Variables, 64 5.1 Definition of Random Variables, 64
CHAPTER 2 Probability Space and Axioms, 10 2.1
Probability Space, 10
2.2
Conditional Probability, 14
2.3
Independence, 18
2.4
Total Probability and Bayes’ Theorem, 20
5.2 Determination of Distribution and
Density Functions, 66 5.3 Properties of Distribution and Density
Functions, 73 5.4 Distribution Functions from Density
Functions, 75 CHAPTER 6
CHAPTER 3 Basic Combinatorics, 25
Continuous Random Variables and Basic Distributions, 79
3.1
Basic Counting Principles, 25
6.1 Introduction, 79
3.2
Permutations, 26
6.2 Uniform Distribution, 79
3.3
Combinations, 28
6.3 Exponential Distribution, 80
CHAPTER 4 Discrete Distributions, 37
6.4 Normal or Gaussian Distribution, 84
CHAPTER 7
4.1
Bernoulli Trials, 37
Other Continuous Distributions, 95
4.2
Binomial Distribution, 38
7.1 Introduction, 95
4.3
Multinomial Distribution, 41
7.2 Triangular Distribution, 95
4.4
Geometric Distribution, 42
7.3 Laplace Distribution, 96
4.5
Negative Binomial Distribution, 44
7.4 Erlang Distribution, 97
vii
viii
Contents
7.5 Gamma Distribution, 99
10.4
Higher-Order Moments, 153
7.6 Weibull Distribution, 101
10.5
Bivariate Gaussian, 154
7.7 Chi-Square Distribution, 102 7.8 Chi and Other Allied Distributions,
104 7.9 Student-t Density, 110 7.10 Snedecor F Distribution, 111
CHAPTER 11 Characteristic Functions and Generating Functions, 155 11.1
Characteristic Functions, 155
11.2
Examples of Characteristic Functions, 157
11.3
Generating Functions, 161
11.4
Examples of Generating Functions, 162
11.5
Moment Generating Functions, 164
11.6
Cumulant Generating Functions, 167
11.7
Table of Means and Variances, 170
7.11 Lognormal Distribution, 112 7.12 Beta Distribution, 114 7.13 Cauchy Distribution, 115 7.14 Pareto Distribution, 117 7.15 Gibbs Distribution, 118 7.16 Mixed Distributions, 118 7.17 Summary of Distributions of
Continuous Random Variables, 119
CHAPTER 12 Functions of a Single Random Variable, 173
CHAPTER 8
12.1
Random Variable g(X ), 173
12.2
Distribution of Y ¼ g(X ), 174
12.3
Direct Determination of Density fY ( y) from fX (x), 194
12.4
Inverse Problem: Finding g(x) Given fX(x) and fY ( y), 200
12.5
Moments of a Function of a Random Variable, 202
Conditional Densities and Distributions, 122 8.1 Conditional Distribution and Density
for P(A)= 0, 122 8.2 Conditional Distribution and Density
for P(A) ¼ 0, 126 8.3 Total Probability and Bayes’ Theorem
for Densities, 131 CHAPTER 9 Joint Densities and Distributions, 135
CHAPTER 13
9.1 Joint Discrete Distribution
Functions of Multiple Random Variables, 206
Functions, 135
13.1
Function of Two Random Variables, Z ¼ g(X,Y ), 206
13.2
Two Functions of Two Random Variables, Z ¼ g(X,Y ), W ¼ h(X,Y ), 222
13.3
Direct Determination of Joint Density fZW(z,w ) from fXY (x,y ), 227
13.4
Solving Z ¼ g(X,Y ) Using an Auxiliary Random Variable, 233
13.5
Multiple Functions of Random Variables, 238
9.2 Joint Continuous Distribution
Functions, 136 9.3 Bivariate Gaussian Distributions, 144
CHAPTER 10 Moments and Conditional Moments, 146 10.1 Expectations, 146 10.2 Variance, 149 10.3 Means and Variances of Some
Distributions, 150
Contents
CHAPTER 14 Inequalities, Convergences, and Limit Theorems, 241 14.1
Degenerate Random Variables, 241
14.2
Chebyshev and Allied Inequalities, 242
14.3
Markov Inequality, 246
14.4
Chernoff Bound, 248
14.5
Cauchy–Schwartz Inequality, 251
14.6
Jensen’s Inequality, 254
14.7
Convergence Concepts, 256
14.8
Limit Theorems, 259
17.4 Diagonalization of
Covariance Matrices, 330 17.5 Simultaneous Diagonalization of
Covariance Matrices, 334 17.6 Linear Estimation of Vector
Variables, 337 CHAPTER 18 Estimation Theory, 340 18.1 Criteria of Estimators, 340 18.2 Estimation of Random
Variables, 342 18.3 Estimation of Parameters (Point
Estimation), 350 CHAPTER 15 Computer Methods for Generating Random Variates, 264
18.4 Interval Estimation (Confidence
Intervals), 364 18.5 Hypothesis Testing (Binary), 373
15.1
Uniform-Distribution Random Variates, 264
15.2
Histograms, 266
15.3
Inverse Transformation Techniques, 269
Random Processes, 406
15.4
Convolution Techniques, 279
19.1 Basic Definitions, 406
15.5
Acceptance –Rejection Techniques, 280
19.2 Stationary Random Processes, 420
CHAPTER 16 Elements of Matrix Algebra, 284 16.1
Basic Theory of Matrices, 284
16.2
Eigenvalues and Eigenvectors of Matrices, 293
16.3 16.4
Vectors and Matrix Differentiations, 301 Block Matrices, 308
18.6 Bayesian Estimation, 384
CHAPTER 19
19.3 Ergodic Processes, 439 19.4 Estimation of Parameters of
Random Processes, 445 19.5 Power Spectral Density, 472
CHAPTER 20 Classification of Random Processes, 490 20.1 Specifications of Random
Processes, 490 20.2 Poisson Process, 492
CHAPTER 17 Random Vectors and Mean-Square Estimation, 311
20.3 Binomial Process, 505 20.4 Independent Increment Process, 507
17.1
Distributions and Densities, 311
20.5 Random-Walk Process, 512
17.2
Moments of Random Vectors, 319
20.6 Gaussian Process, 521
17.3
Vector Gaussian Random Variables, 323
20.7 Wiener Process (Brownian
Motion), 523
ix
x
Contents
20.8 Markov Process, 527
CHAPTER 23
20.9 Markov Chain, 536
Probabilistic Methods in Transmission Tomography, 666
20.10
Martingale Process, 551
23.1
Introduction, 666
20.11
Periodic Random Process, 557
23.2
Stochastic Model, 667
20.12
Aperiodic Random Process (Karhunen – Loeve Expansion), 563
23.3
Stochastic Estimation Algorithm, 671
23.4
Prior Distribution PfMg, 674
23.5
Computer Simulation, 676
23.6
Results and Conclusions, 678
23.7
Discussion of Results, 681
23.8
References for Chapter 23, 681
CHAPTER 21 Random Processes and Linear Systems, 574 21.1 Review of Linear Systems, 574 21.2 Random Processes through
Linear Systems, 578 21.3 Linear Filters, 592 21.4 Bandpass Stationary Random
Processes, 606
APPENDIXES A
A Fourier Transform Tables, 683
B Cumulative Gaussian Tables, 687 C Inverse Cumulative Gaussian Tables, 692 D
Inverse Chi-Square Tables, 694
Weiner and Kalman Filters, 625
E
Inverse Student-t Tables, 701
22.1 Review of Orthogonality
F Cumulative Poisson Distribution, 704
CHAPTER 22
Principle, 625
G
Cumulative Binomial Distribution, 708
22.2 Wiener Filtering, 627 22.3 Discrete Kalman Filter, 647 22.4 Continuous Kalman Filter, 660
References, 714 Index, 716
Preface
Many good textbooks exist on probability and random processes written at the undergraduate level to the research level. However, there is no one handy and ready book that explains most of the essential topics, such as random variables and most of their frequently used discrete and continuous probability distribution functions; moments, transformation, and convergences of random variables; characteristic and generating functions; estimation theory and the associated orthogonality principle; vector random variables; random processes and their autocovariance and cross-covariance functions; stationarity concepts; and random processes through linear systems and the associated Wiener and Kalman filters. Engineering practitioners and students alike have to delve through several books to get the required formulas or tables either to complete a project or to finish a homework assignment. This book may alleviate this difficulty to some extent and provide access to a compendium of most distribution functions used by communication engineers, queuing theory specialists, signal processing engineers, biomedical engineers, and physicists. Probability tables with accuracy up to nine decimal places are given in the appendixes to enhance the utility of this book. A particular feature is the presentation of commonly occurring Fourier transforms where both the time and frequency functions are drawn to scale. Most of the theory has been explained with figures drawn to scale. To understand the theory better, more than 300 examples are given with every step explained clearly. Following the adage that a figure is worth more than a thousand words, most of the examples are also illustrated with figures drawn to scale, resulting in more than 400 diagrams. This book will be of particular value to graduate and undergraduate students in electrical, computer, and civil engineering as well as students in physics and applied mathematics for solving homework assignments and projects. It will certainly be useful to communication and signal processing engineers, and computer scientists in an industrial setting. It will also serve as a good reference for research workers in biostatistics and financial market analysis. The salient features of this book are .
Functional and statistical independence of random variables are explained.
.
Ready reference to commonly occurring density and distribution functions and their means and variances is provided.
.
A section on Benford’s logarithmic law, which is used in detecting tax fraud, is included.
.
More than 300 examples, many of them solved in different ways to illustrate the theory and various applications of probability, are presented. Most examples have been substantiated with graphs drawn to scale.
.
xi
xii
Preface .
More than 400 figures have been drawn to scale to improve the clarity of understanding the theory and examples.
.
Bounds on the tails of Gaussian probability have been carefully developed. A chapter has been devoted to computer generation of random variates.
. .
.
Another chapter has been devoted to matrix algebra so that some estimation problems such as the Kalman filter can be cast in matrix framework. Estimation problems have been given considerable exposure.
.
Random processes defined and classified. A section on martingale processes with examples has been included.
.
Markov chains with interesting examples have been discussed.
.
Wiener and Kalman filtering have been discussed with a number of examples. Important properties are summarized in tables.
.
. . .
.
The final chapter is on applications of probability to tomographic imaging. The appendixes consist of probability tables with nine-place decimal accuracy for the most common probability distributions. An extremely useful feature are the Fourier transform tables with both time and frequency functions graphed carefully to scale.
After the introduction of functional independence in Section 1.2, the differences between functional and statistical independences are discussed in Section 2.3 and clarified in Example 2.3.2. The foundation for permutations and combinations are laid out in Chapter 3, followed by discrete distributions in Chapter 4 with an end-of-chapter summary of discrete distributions. Random variables are defined in Chapter 5, and most of the frequently occurring continuous distributions are explained in Chapters 6 and 7. Section 6.4 discusses the new bounds on Gaussian tails. A comprehensive table is given at the end of Chapter 7 for all the continuous distributions and densities. After discussion of conditional densities, joint densities, and moments in Chapters 8 –10, characteristic and other allied functions are explained in Chapter 11 with a presentation of an end-of-chapter table of means and variances of all discrete and continuous distributions. Functions of random variables are discussed in Chapters 12 and 13 with numerous examples. Chapter 14 discusses the various bounds on probabilities along with some commonly occurring inequalities. Computer generation of random variates is presented in Chapter 15. Elements of matrix algebra along with vector and matrix differentiations are considered in Chapter 16. Vector random variables and diagonalization of covariance matrices and simultaneous diagonalization of two covariance matrices are taken up in Chapter 17. Estimation and allied hypothesis testing are discussed in Chapter 18. After random processes are explained in Chapter 19, they are carefully classified in Chapter 20, with Section 20.10 presenting martingale processes that find wide use in financial engineering. Chapter 21 discusses the effects of passing random processes through linear systems. A number of examples illustrate the basic ideas of Wiener and Kalman filters in Chapter 22. The final chapter, Chapter 23, presents a practical application of probability to tomographic imaging. The appendixes include probability tables up to nine decimal places for Gaussian, chi-square, Student-t, Poisson, and binomial distributions, and Fourier transform tables with graphs for time and frequency functions carefully drawn to scale. This book is suitable for students and practicing engineers who need a quick reference to any probability distribution or table. It is also useful for self-study where a number of
Preface
xiii
carefully solved examples illustrate the applications of probability. Almost every topic has solved examples. It can be used as an adjunct to any textbook on probability and may also be prescribed as a textbook with homework problems drawn from other sources. During the more than four decades that I have been continuously teaching probability and random processes, many authors who have written excellent textbooks have influenced me to a considerable extent. Many of the ideas and concepts that are expanded in this book are the direct result of this influence. I owe a scientific debt of gratitude to all the authors who have contributed to this ubiquitous and exciting field of probability. Some of these authors are listed in the reference, and the list is by no means exhaustive. Most of the graphs and all the probability tables in this book were created with Mathcad software. All algebraic calculations were verified with Mathcad software. Mathcad and Mathsoft are registered trademarks of Mathsoft Engineering and Education, Inc., http://www.mathcad. com. While facing extreme personal difficulties in writing this book, the unequivocal support of my entire family have been a source of inspiration in finishing it. I acknowledge with great pleasure the support given to me by the Wiley staff. George Telecki welcomed the idea of this book; Rachel Witmer did more than her share of keeping me in good humor with her cheerful disposition in spite of the ever-expanding deadlines; and Kellsee Chu who did an excellent job in managing the production of this book. Finally, the enthusiastic support given to me by the Series Editor Emmanuel Desurvire was very refreshing. This is my second book with Wiley, and the skills of their copyeditors and staff who transform highly mathematical manuscript into a finished book continue to amaze me. I have revised this book several times and corrected errors. Nevertheless, I cannot claim that it is error-free since correcting errors in any book is a convergent process. I sincerely hope that readers will bring to my attention any errors of commission or omission that they may come across. VENKATARAMA KRISHNAN Chelmsford, Massachusetts March 2006
CHAPTER
1
Sets, Fields, and Events
B 1.1
SET DEFINITIONS
The concept of sets play an important role in probability. We will define a set in the following paragraph. Definition of Set
A set is a collection of objects called elements. The elements of a set can also be sets. Sets are usually represented by uppercase letters A, and elements are usually represented by lowercase letters a. Thus A ¼ {a1 ;a2 ; . . . , an }
(1:1:1)
will mean that the set A contains the elements a1,a2, . . . , an. Conversely, we can write that ak is an element of A as ak [ A
(1:1:2)
ak A
(1:1:3)
and ak is not an element of A as
A finite set contains a finite number of elements, for example, S ¼ f2,4,6g. Infinite sets will have either countably infinite elements such as A ¼ fx : x is all positive integersg or uncountably infinite elements such as B ¼ fx : x is real number 20g. Example 1.1.1
The set A of all positive integers less than 7 is written as A ¼ {x : x is a positive integer ,7}: finite set:
Example 1.1.2
The set N of all positive integers is written as N ¼ {x : x is all positive integers}: countably infinite set:
Example 1.1.3
The set R of all real numbers is written as R ¼ {x : x is real}: uncountably infinite set:
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
1
2
Sets, Fields, and Events
Example 1.1.4
The set R 2 of real numbers x, y is written as R2 ¼ {(x,y) : x is real, y is real}
Example 1.1.5
The set C of all real numbers x,y such that x þ y 10 is written as C ¼ {(x,y) : x þ y 10}: uncountably infinite set:
Venn Diagram
Sets can be represented graphically by means of a Venn diagram. In this case we assume tacitly that S is a universal set under consideration. In Example 1.1.5, the universal set S ¼ fx : x is all positive integersg. We shall represent the set A in Example 1.1.1 by means of a Venn diagram of Fig. 1.1.1. Empty Set
An empty is a set that contains no element. It plays an important role in set theory and is denoted by 1. The set A ¼ f0g is not an empty set since it contains the element 0. Cardinality
The number of elements in the set A is called the cardinality of set A, and is denoted by jAj. If it is an infinite set, then the cardinality is 1. Example 1.1.6 The cardinality of the set A ¼ f2,4,6g is 3, or jAj ¼ 3. The cardinality of set R ¼ fx : x is realg is 1. Example 1.1.7
The cardinality of the set A ¼ fx : x is positive integer ,7g is jAj ¼ 6.
Example 1.1.8 The cardinality of the set B ¼ fx : x is a real number ,10g is infinity since there are infinite real numbers ,10. Subset
A set B is a subset of A if every element in B is an element of A and is written as B # A. B is a proper subset of A if every element of A is not in B and is written as B , A.
FIGURE 1.1.1
1.2 Set Operations
3
FIGURE 1.1.2 Equality of Sets
Two sets A and B are equal if B # A and A # B, that is, if every element of A is contained in B and every element of B is contained in A. In other words, sets A and B contain exactly the same elements. Note that this is different from having the same cardinality, that is, containing the same number of elements. Example 1.1.9 The set B ¼ f1,3,5g is a proper subset of A ¼ f1,2,3,4,5,6g, whereas the set C ¼ fx : x is a positive even integer 6g and the set D ¼ f2,4,6g are the same since they contain the same elements. The cardinalities of B, C, and D are 3 and C ¼ D. We shall now represent the sets A and B and the sets C and D in Example 1.1.9 by means of the Venn diagram of Fig. 1.1.2 on a suitably defined universal set S. Power Set
The power set of any set A is the set of all possible subsets of A and is denoted by PS(A). Every power set of any set A must contain the set A itself and the empty set 1. If n is the cardinality of the set A, then the cardinality of the power set jPS(A)j ¼ 2n. Example 1.1.10 If the set A ¼ f1,2,3g then PS(A) ¼ f1, (1,2,3), (1,2), (2,3), (3,1), (1), (2), (3)g. The cardinality jPS(A)j ¼ 8 ¼ 23.
B 1.2
SET OPERATIONS
Union
Let A and B be sets belonging to the universal set S. The union of sets A and B is another set C whose elements are those that are in either A or B, and is denoted by A < B. Where there is no confusion, it will also be represented as A þ B: A < B ¼ A þ B ¼ {x : x [ A or x [ B}
(1:2:1)
4
Sets, Fields, and Events
FIGURE 1.2.1
Example 1.2.1 The union of sets A ¼ f1,2,3g and B ¼ f2,3,4,5g is the set C ¼ A < B ¼ f1,2,3,4,5g. Intersection
The intersection of the sets A and B is another set C whose elements are the same as those in both A and B and is denoted by A > B. Where there is no confusion, it will also be represented by AB. A > B ¼ AB ¼ {x : x [ A and x [ B}
(1:2:2)
Example 1.2.2 The intersection of the sets A and B in Example 1.2.1 is the set C ¼ f2,3g Examples 1.2.1 and 1.2.2 are shown in the Venn diagram of Fig. 1.2.1. Mutually Exclusive Sets
Two sets A and B are called mutually exclusive if their intersection is empty. Mutually exclusive sets are also called disjoint. A>B¼1
(1:2:3)
One way to determine whether two sets A and B are mutually exclusive is to check whether set B can occur when set A has already occurred and vice versa. If it cannot, then A and B are mutually exclusive. For example, if a single coin is tossed, the two sets, fheadsg and ftailsg, are mutually exclusive since ftailsg cannot occur when fheadsg has already occurred and vice versa. Independence
We will consider two types of independence. The first is known as functional independence [42]. Two sets A and B can be called functionally independent if the occurrence of B does not in any way influence the occurrence of A and vice versa. The second one is statistical independence, which is a different concept that will be defined later. As an example, the tossing of a coin is functionally independent of the tossing of a die because they do not depend on each other. However, the tossing of a coin and a die are not mutually exclusive since any one can be tossed irrespective of the other. By the same token, pressure and temperature are not functionally independent because the physics of the problem, namely, Boyle’s law, connects these quantities. They are certainly not mutually exclusive.
Numbers in brackets refer to bibliographic entries in the References section at the end of the book.
1.2 Set Operations
5
Cardinality of Unions and Intersections
We can now ascertain the cardinality of the union of two sets A and B that are not mutually exclusive. The cardinality of the union C ¼ A < B can be determined as follows. If we add the cardinality jAj to the cardinality jBj, we have added the cardinality of the intersection jA > Bj twice. Hence we have to subtract once the cardinality jA > Bj as shown in Fig. 1.2.2. Or, in other words jA < Bj ¼ jAj þ jBj jA > Bj
(1.2.4a)
In Fig. 1.2.2 the cardinality jAj ¼ 9 and the cardinality jBj ¼ 11 and the cardinality jA < Bj is 11 þ 9 2 4 ¼ 16. As a corollary, if sets A and B are mutually exclusive, then the cardinality of the union is the sum of the cardinalities; or jA < Bj ¼ jAj þ jBj
(1.2.4b)
The generalization of this result to an arbitrary union of n sets is called the inclusion– exclusion principle, given by n n n n X [ X X Ai > A j þ A i > A j > Ak jA i j Ai ¼ i¼1 i¼1 i, j¼1 i, j, k¼1 i=j
(+1)n
i=j=k
n X Ai > A j > A k > An
(1:2:5a)
i, j, k¼1 i=j=k...=n
If the sets fAig are mutually exclusive, that is, Ai > Aj ¼ 1 for i = j, then we have n n X [ jA j (1:2:5b) A i ¼ i¼1 i¼1 i This equation is illustrated in the Venn diagram for n ¼ 3 in Fig. 1.2.3, where if jA < B < Cj equals, jAj þ jBj þ jCj then we have added twice the cardinalites of jA > Bj, jB > Cj and jC > Aj. However, if we subtract once jA > Bj, jB > Cj, and jC > Aj and write jA < B < Cj ¼ jAj þ jBj þ jCj jA > Bj jB > Cj jC > Aj then we have subtracted jA > B > Cj thrice instead of twice. Hence, adding jA > B > Cj we get the final result: jA < B < Cj ¼ jAj þ jBj þ jCj jA > Bj jB > Cj jC > Aj jA > B > Cj (1:2:6)
FIGURE 1.2.2
6
Sets, Fields, and Events
FIGURE 1.2.3
Example 1.2.3 In a junior class, the number of students in electrical engineering (EE) is 100, in math (MA) 50, and in computer science (CS) 150. Among these 20 are taking both EE and MA, 25 are taking EE and CS, and 10 are taking MA and CS. Five of them are taking EE, CS and MA. Find the total number of students in the junior class. From the problem we can identify the sets A ¼ EE students, B ¼ MA students, and C ¼ CS students, and the corresponding intersections are A > B, B > C, C > A, and A > B > C. Here, jAj ¼ 100, jBj ¼ 50, and jCj ¼ 150. We are also given jA > Bj ¼ 20, jB > Cj ¼ 10, and jC > Aj ¼ 25 and finally jA > B > Cj ¼ 5. Using Eq. (1.2.6) the total number of students are 100 þ 50 þ 150 2 20 2 10 2 25 þ 5 ¼ 250. Complement
If A is a subset of the universal set S, then the complement of A denoted by A is the elements in S not contained in A; or A ¼ S A ¼ {x : x A , S and x [ S}
(1:2:7)
Example 1.2.4 If the universal set S ¼ f1,2,3,4,5,6g and A ¼ f2,4,5g, then the complement of A is given by A ¼ f1,3,6g. Difference
The complement of a subset A , S as given by Eq. (1.2.7) is the difference between the universal set S and the subset A. The difference between any two sets A and B denoted by A 2 B is the set C containing those elements that are in A but not in B and, B 2 A is the set D containing those elements in B but not in A: C ¼ A B ¼ {x : x [ A and x B} D ¼ B A ¼ {x : x [ B and x A}
(1:2:8)
A 2 B is also called the relative complement of B with respect to A and B 2 A is called the relative complement of A with respect to B. It can be verified that A 2 B = B 2 A. Example 1.2.5 If the set A is given as A ¼ f2,4,5,6g and B is given as B ¼ f5,6,7,8g, then the difference A 2 B ¼ f2,4g and the difference B 2 A ¼ f7,8g. Clearly, A 2 B = B 2 A.
1.2 Set Operations
7
FIGURE 1.2.4 Symmetric Difference
The symmetric difference between sets A and B is written as ADB and defined by ADB ¼ (A B) < (B A) ¼ {x : x [ A and x B) or {x : x [ B and x A) (1:2:9) Example 1.2.6 The symmetric difference between the set A given by A ¼ f2,4,5,6g and B given by B ¼ f5,6,7,8g in Example 1.2.5 is ADB ¼ f2,4g < f7,8g ¼ f2,4,7,8g. The difference and symmetric difference for Examples 1.2.5 and 1.2.6 are shown in Fig. 1.2.4. Cartesian Product
This is a useful concept in set theory. If A and B are two sets, the Cartesian product A B is the set of ordered pairs (x,y) such that x [ A and y [ B and is defined by A B ¼ {(x,y) : x [ A, y [ B}
(1:2:10)
When ordering is considered the cartesian product A B = B A. The cardinality of a Cartesian product is the product of the individual cardinalities, or jA Bj ¼ jAj . jBj. Example 1.2.7 If A ¼ f2,3,4g and B ¼ f5,6g, then C ¼ A B ¼ f(2,5), (2,6), (3,5), (3,6), (4,5), (4,6)g with cardinality 3 2 ¼ 6. Similarly, D ¼ B A ¼ f(5,2), (5,3), (5,4), (6,2), (6,3), (6,4)g with the same cardinality of 6. However, C and D do not contain the same ordered pairs. Partition
S A partition is a collection T of disjoint sets fAi, i ¼ 1, . . . , ng of a set S such that Ai over all i equals S and Ai Aj , with i = j empty. Partitions play a useful role in conditional probability and Bayes’ theorem. Figure 1.2.5 shows the concept of a partition.
FIGURE 1.2.5
8
Sets, Fields, and Events
FIGURE 1.2.6
TABLE 1.2.1
Table of set properties
Property
Equation
Identity
A<1¼A A>S¼A
Domination
A>1¼1 A<S¼S
Idempotent
A>A¼A A
Complementation
A ¼A
Commutative
AB¼B>A
Associative
A < (B < C ) ¼ (A < B) < C A > (B > C ) ¼ (A > B) > C
Distributive
A > (B < C ) ¼ (A > B) < (A > C ) A < (B > C ) ¼ (A < B) > (A < C )
Noncommutative
A2B = B2A
De Morgan’s law
AB A>B¼A
Example 1.2.8 If set S ¼ f1,2,3,4,5,6,7,8,9,10g, then the collections of sets fA1, A2, A3, A4g where A1 ¼ f1,3,5g, A2 ¼ f7,9g, A3 ¼ f2,4,6g, A4 ¼ f8,10g is a partition of S as shown in Fig. 1.2.6. A tabulation of set properties is shown on Table 1.2.1. Among the identities, De Morgan’s laws are quite useful in simplifying set algebra.
B 1.3
SET ALGEBRAS, FIELDS, AND EVENTS
Boolean Field
We shall now consider a universal set S and a collection F of subsets of S consisting of the sets fAi: i ¼ 1,2, . . . , n,n þ 1, . . .g. The collection F is called a Boolean field if the
1.3 Set Algebras, Fields, and Events
9
following two conditions are satisfied: 1. If Ai [ F, then Ai [ F. 2. If A1 [ F and A2 [ F, then A1 < A2 [ F. 3. If fAi , i ¼ 1, . . . , ng [ F, then < ni¼1 Ai [ F. Let us see the consequences of these conditions being satisfied. If A1 [ F and A1 F, then A1 < A1 ¼ S [ F. If S [ F, then S ¼ 1 [ F. If A1 < A2 [ F, then by condition 1 A1 < A2 [ F, and by De Morgan’s law, A1 < A2 [ F. Hence A1 > A2 [ F. Also A1 2 A2 [ F and (A1 2 A2) < (A2 2 A1) [ F. Thus the conditions listed above are sufficient for any field F to be closed under all set operations. Sigma Field
The preceding definition of a field covers only finite set operations. Condition 3 for finite additivity may not hold for infinite additivity. For example, the sum of 1 þ 2 þ 22 þ 23 þ 24 ¼ (25 2 1)/(2 2 1) ¼ 31, but the infinite sum 1 þ 2 þ 22 þ 23 þ 24 þ . . . diverges. Hence there is a need to extend finite additivity concept to infinite set operations. Thus, we have a s field if the following extra condition is imposed for infinite additivity: 4. If A1, A2, . . . An, . . . [ F then <1 i¼1 Ai [ F Many s fields may contain the subsets fAig of S, but the smallest s field containing the subsets fAig is called the Borel s field. The smallest s field for S by itself is F ¼ fS, 1g. Example 1.3.1 We shall assume that S ¼ f1,2,3,4,5,6g. Then the collection of the following subsets of S, F ¼ fS, 1, (1,2,3), (4,5,6)g is a field since it satisfies all the set operations such as (1,2,3) < (4,5,6) ¼ S, ð 1,2,3 Þ ¼ (4,5,6). However, the collection of the following subsets of S, F1 ¼ {S, 1, (1,2,3), (4,5,6), ð2Þ} will not constitute a field because (2) < (4,5,6) ¼ (2,4,5,6) is not in the field. But we can adjoin the missing sets and make F1 into a field. This is known as completion. In the example above, if we adjoin the collection of sets F2 ¼ {(2,4,5,6), (1,3)} to F1 , then the resulting collection F ¼ F1 < F2 ¼ {S, 1, (1,2,3), (4,5,6), {2}, (2,4,5,6), (1,3)} is a field. Event
Given the universal set S and the associated s field F, all subsets of S belonging to the field F are called events. All events are subsets of S and can be assigned a probability, but not all subsets of S are events unless they belong to an associated s field.
CHAPTER
2
Probability Space and Axioms
B 2.1
PROBABILITY SPACE
A probability space is defined as the triplet fS, F, Pg, where S is the sample space, F is the field of subsets of S, and P is a probability measure. We shall now define these elements. Sample Space S
Finest-grain, mutually exclusive, and collectively exhaustive listing of all possible outcomes of a mathematical experiment is called the sample space. The outcomes can be either finite, countably infinite, or uncountably infinite. If S is countable, we call it a discrete sample space; if it is uncountable, we call it continuous sample space. Examples of Discrete Sample Space
Example 2.1.1 When a coin is tossed, the outcomes are heads {H} or tails {T}. Thus the sample space S ¼ {H,T}. Example 2.1.2 When a die is tossed, the outcomes are 1,2,3,4,5,6. Thus the sample space is S ¼ {1,2,3,4,5,6}. Example 2.1.3 The set of all positive integers is countable, and the sample space is S ¼ fx : x is a positive integerg Examples of Continuous Sample Space
Example 2.1.4 The set of all real numbers between 0 and 2 is the sample space S ¼ fx : 0 x 2g. Example 2.1.5 The set of all real numbers x and y such that x is between 0 and 1 and y is between 1 and 2 is given by S ¼ fx, y : 0 x 1, 1 y 2g. The continuous sample space given in Examples 2.1.4 and 2.1.5 is shown in Fig. 2.1.1.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
10
2.1 Probability Space
11
FIGURE 2.1.1
Field F
A field has already been defined in Section 1.3. We shall assume that a proper Borel s field can be defined for all discrete and continuous sample spaces.
Probability Measure P
A probability measure is defined on the field F of events of the sample space S. It is a mapping of the sample space to the real line between 0 and 1 such that it satisfies the following three Kolmogorov axioms for all events belonging to the field F: Axiom 1: If A belongs to F; then P(A) 0: nonnegativity:
(2:1:1)
Axiom 2: The probability of the sample space equals 1. P(S) ¼ 1: normalization:
(2:1:2)
Axiom 3a: If A and B are disjoint sets belonging to F, that is, A > B ¼ 1, then P{A < B} ¼ P(A) þ P(B): Boolen additivity:
(2:1:3)
Axiom 3b: If fA1, A2, . . . g is a sequence of sets belonging to the field 1 such that Ai > Aj ¼ 1 for all i = j, then P fA 1 < A 2 < < A i < g ¼
1 X
PfA j g: sigma additivity:
(2:1:4)
i¼1
From these axioms we can write the following corollaries: Corollary 1: P{1}
(2:1:5)
Note that the probability of the empty event is zero, but if the probability of an event A is zero, we cannot conclude that A ¼ 1. All sets with zero probability are defined as null sets. An empty set is also a null set, but null sets need not be empty. Corollary 2. Since A < A ¼ S and A > A ¼ 1, we can write P(A) ¼ 1 P(A)
(2:1:6)
12
Probability Space and Axioms
Corollary 3. For any A and B that are subsets of S, not mutually exclusive, we can write Pf A < Bg ¼ P{A < A > B} ¼ P{A} þ P{A > B} P{B} ¼ P{(A > B) < (A > B)} ¼ P{A > B} þ P{A > B} Eliminating P{A < B} between the two equations, we obtain P{A < B} ¼ P{A} þ P{B} P{A > B}
(2:1:7)
Clearly, if P{A > B} ¼ 0 implying A and B are mutually exclusive, then Eq. (2.1.7) reduces to Kolmogorov’s axiom 3a. If the probabilities P{A} and P{B} are equal, then sets A and B are equal in probability. This does not mean that A is equal to B.
The probability of the union of three sets can be calculating from the Venn diagram for the three sets shown in Fig. 1.2.3 and redrawn in Fig. 2.1.2. The results of the analysis for the cardinalities given in Eq. (1.2.6), can be extended to finding the P(A < B < C). We subtract {P(A > B) þ P(B > C) þ P(C > A)} from {P(A) þ P(B) þ P(C)} and add P(A > B > C), resulting in
P{A < B < C} ¼ P{A} þ P{B} þ P{C} P{A > B} P{B > C} P{C > A} þ P{A > B > C}
FIGURE 2.1.2
(2:1:8)
2.1 Probability Space
13
Using the inclusion– exclusion principle of Eq. (1.2.5a), we can also generalize Eq. (2.1.8) to the probabilities of the union of n sets A1, A2, . . . , An as follows: Pfni¼1 Ai g ¼
n X
P{Ai }
i¼1
n X
P{Ai > A j } þ
i, j¼1 i=j
(+1)n
n X
n X
P{Ai > A j > Ak }
i, j, k¼1 i=j=k
P{Ai > A j > Ak > An }
(2:1:9)
i, j, k¼1 i=j=k...=n
Example 2.1.6 A fair coin is tossed 3 times. Since each toss is a functionally independent trial and forms a Cartesian product, the cardinality of the three tosses is 2 2 2 ¼ 8 and the sample space consists of the following 8 events: S ¼ {½HHH, ½HHT, ½HTT, ½HTH, ½THH, ½TTH, ½THT, ½TTT} In this sample space P{2 heads} ¼ P{½HHH, ½HHT, ½HTH, ½THH} ¼
4 8
P{both heads and tails} ¼ {½HHT, ½HTT, ½HTH, ½THH, ½TTH, ½THT} ¼
6 8
Example 2.1.7 A telephone call occurs at random between 9:00 a.m. and 11:00 a.m. Hence the probability that the telephone call occurs in the interval 0 , t t0 is given by t0 =120. Thus the probability that the call occurs between 9:30 a.m. and 10:45 a.m. is given by: P{30 , t 105} ¼
105 30 75 5 ¼ ¼ 120 120 8
Example 2.1.8 Mortality tables give the probability density that a person who is born today assuming that the lifespan is 100 years as f (t) ¼ 3 109 t2 (100 t2 )u(t) and the distribution function that the person will die at the age t0 years as ð t0 F(t0 ) ¼ P{t t0 } ¼
3 109 t2 (100 t2 ) dt
0
These probabilities are shown in Fig. 2.1.3. The probability that this person will die between the ages 70 to 80 is given by P{70 , t 80} ¼ 0:1052, obtained from the following integral: ð 80 P{70 , t 80} ¼ F(80) F(70) ¼ 3 109 t2 (100 t2 ) dt ¼ 0:1052 70
The density and distribution functions are shown in Fig. 2.1.3.
14
Probability Space and Axioms
FIGURE 2.1.3
B 2.2
CONDITIONAL PROBABILITY
The conditional probability of an event B conditioned on the occurrence of another event B is defined by
P{B j A} ¼
P{B > A} P{A}
P{B > A} ¼ P{B j A}P{A}
9 > = > ;
if P{A} = 0
(2:2:1)
If P{A} ¼ 0, then the conditional probability P{B j A} is not defined. Since P{B j A} is a probability measure, it also satisfies Kolmogorov axioms. Conditional probability P{B j A} can also be interpreted as the probability of the event B in the reduced sample space S1 ¼ A. Sometimes it is simpler to use the reduced sample space to solve conditional probability problems. We shall use both these interpretations in obtaining conditional probabilities as shown in the following examples.
2.2
Conditional Probability
15
FIGURE 2.2.1
Example 2.2.1 In the tossing of a fair die, we shall calculate the probability that 3 has occurred conditioned on the toss being odd. We shall define the following events: A ¼ {odd number}: B ¼ 3 1 2
1 6
P{A} ¼ and P{B} ¼ and the event A > B ¼ {3} and hence 1 p{3} 1 ¼6¼ P{3 j odd} ¼ 1 P{odd} 3 2 Note that P{2 j odd} ¼ 0 since 2 is an impossible event given that an odd number has occurred. Also note that P{3 j 3} ¼ 1. The conditional probability of 3 occurring, given that 3 has occurred is obviously equal to 1. We can also find the solution using the concept of reduced sample space. Since we are given that an odd number has occurred, we can reduce the sample space from S ¼ {1,2,3,4,5,6} to S1 ¼ {1,3,5} as shown in Fig. 2.2.1. The probability of 3 in this reduced sample space is 13, agreeing with the previous result. Example 2.2.2 Consider a family of exactly two children. We will find the probabilities (1) that both are girls, (2) both are girls given that one of them is a girl, (3) both are girls given that the elder child is a girl, and (4) both are girls given that both are girls. The sample space S has 4 points {gg, gb, bg, bb}, where g is girl and b is boy. The event B ¼ fboth children are girlsg. The conditioning event A will depend on the following four cases: 1. The event A ¼ S. Hence P{B} ¼ 14. 2. The event A ¼ fone child is a girlg. The reduced sample space is S1 ¼ fgg, gb, bgg and the conditional probability P{B j A} ¼ 13. 3. The event A ¼ felder child is a girlg. In this case, the reduced sample space is S2 ¼ {gg, gb} and P{B j A} ¼ 12. 4. The event ¼ fboth are girlsg. The reduced sample space has only one point, namely, S3 ¼ {gg} and P{B j A} ¼ 1. As our knowledge of the experiment increases by the conditioning event, the uncertainty decreases and the probability increases from 0.25, through 0333, 0.5, and finally to 1, meaning that there is no uncertainty.
16
Probability Space and Axioms
Example 2.2.3 The game of craps as played in Las Vegas has the following rules. A player rolls two dice. He wins on the first roll if he throws a 7 or a 11. He loses if the first throw is a 2, 3, or 12. If the first throw is a 4, 5, 6, 8, 9, or 10, it is called a point and the game continues. He goes on rolling until he throws the point for a win or a 7 for a loss. We have to find the probability of the player winning. We will solve this problem both from the definition of conditional probability and the reduced sample space. Figure 2.2.2 shows the number of ways the sums of the pips on the two dice can equal 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, and their probabilities. Solution Using Definition of Conditional Probability. The probability of winning in the first throw is P{7} þ P{11} ¼
6 2 8 þ ¼ ¼ 0:22222 36 36 36
The probability of losing in the first throw is P{2} þ P{3} þ P{12} ¼
1 2 1 4 þ þ ¼ ¼ 0:11111 36 36 36 36
To calculate the probability of winning in the second throw, we shall assume that i is the point with probability p. The probability of not winning in any given throw after the first is given by r ¼ P{not i and not 7} ¼ 1 p 16. We compute the conditional probability P{win j i in first throw} ¼ p þ rp þ r 2 p þ ¼
p p ¼ 1 r p þ 16
as an infinite geometric series: P{win in second or subsequent throws} ¼
p2 p þ 16
The probability p of making the point i ¼ 4,5,6,8,9,10 is obtained from Fig. 2.2.2.
FIGURE 2.2.2
2.2
Conditional Probability
17
Thus, for i ¼ 4,5,6, we obtain
2
2
2
3 P{win after point i ¼ 4} ¼ 36
P{win after point i ¼ 5} ¼
P{win after point i ¼ 6} ¼
4 36 5 36
3 1 1 þ ¼ 36 6 36 4 1 2 þ ¼ 36 6 45 5 1 25 þ ¼ 36 6 396
with similar probabilities for 8,9,10. Thus the probability of winning in craps is P{win} ¼ P{win in roll 1} þ P{win in roll 2,3, . . .} 8 1 2 25 244 ¼ þ2 þ þ ¼ 0:4929 ¼ 36 36 45 396 495 Solution Using Reduced Sample Space. We can now use the reduced sample space to arrive at the same result in a simpler manner. After the point i has been rolled in the first throw, the reduced sample space consists of (i 2 1 þ 6) points for i ¼ 4,5,6 and (13 2 i þ 6) points for i ¼ 8,9,10 in which the game will terminate. We can see from the figure that there are 6 ways of rolling 7 for a loss, (i 2 1, i ¼ 4,5,6) ways of rolling the point for a win, and (13 2 i, i ¼ 8,9,10) ways of rolling the point for a win. Thus P{win in roll 2,3, . . . j i in roll 1} ¼ P{W j i} 8 i1 > , < ¼ i1þ6 > : 13 i , 13 i þ 6
i ¼ 4,5,6 i ¼ 8,9,10
4 5 5 4 Thus, P{W j 4} ¼ 39 , P{W j 5} ¼ 10 , P{W j 6} ¼ 11 , P{W j 8} ¼ 11 , P{W j 9} ¼ 10 , 3 P{W j 10} ¼ 9. Hence probability of a win after the first roll is P{W j i} P{i}: Hence
3 3 4 4 5 5 134 þ þ ¼ 9 36 10 36 11 36 495 3 3 4 4 5 5 8 134 244 þ ¼ 0:4929 P{win} ¼ 2 þ þ ¼ þ 9 36 10 36 11 36 36 495 495
P{win after first roll} ¼ 2
a result obtained with comparative ease. Example 2.2.4 We shall continue Example 2.1.8 and find the probability that a person will die between the ages 70 and 80 given that this individual has lived upto the age 70. We will define the events A and B as follows: A ¼ {person has lived upto 70} B ¼ {person dies between ages 70 and 80}
18
Probability Space and Axioms
The required conditional probability equals the number of persons who die between the ages 70 and 80 divided by the number of persons who have lived upto the age 70:
P{B j A} ¼
P{70 , t 80 and t 70} P{70 , t 80} ¼ P{t 70} P{t 70} Ð 80
70 ¼ Ð 100 70
3 109 t2 (100 t2 )dt 3
109 t2 (100
t2 )dt
¼
0:105 ¼ 0:644 0:163
This type of analysis is useful in setting up actuarial tables for life insurance purposes.
B 2.3
INDEPENDENCE
We have already seen the concept of functional independence of sets in Chapter 1. We will now define statistical independence of events. If A and B are two events in a field of events F, then A and B are statistically independent if and only if P{A > B} ¼ P{A}P{B}
(2:3:1)
Statistical independence neither implies nor is implied by functional independence. Unless otherwise indicated, we will call statistically independent events “independent” without any qualifiers. Three events A1 ,A2 ,A3 are independent if they satisfy, in addition to P{A1 > A2 > A3 } þ P{A1 }P{A2 }P{A3 }
(2:3:2)
the pairwise independence P{Ai > A j } ¼ P{Ai }P{A j },
i, j ¼ 1,2,3, i = j
(2:3:3)
Note that if A and B are mutually exclusive, then, from Kolmogorov’s axiom 3a, we obtain P{A < B} ¼ P{A} þ P{B}
(2:3:4)
The concepts of independence and mutual exclusivity are fundamental to probability theory. For any two sets A and B, we recall from Eq. (2.1.7) that P{A < B} ¼ P{A} þ P{B} P{A > B}
(2:1:7)
If A and B are mutually exclusive, then P{A > B} ¼ 0, but if they are independent, then P{A > B} ¼ P{A} P{B}. If this has to be zero for mutual exclusivity, then one of the probabilities must be zero. Therefore, we conclude that mutual exclusivity and independence are incompatible unless we are dealing with null events. We shall now give examples to clarify the concepts of functional independence and statistical independence. Example 2.3.1 The probability of an ace on the throw of a die is 16. The probability of a head on the throw of a coin is 12. Since the throw of a die is not dependent on the throw of a coin, we have functional independence and the combined sample space is the Cartesian product with 12 elements as shown in Table 2.3.1.
2.3 Independence
19
TABLE 2.3.1 1, H
2, H
3, H
4, H
5, H
6, H
1, T
2, T
3, T
4, T
5, T
6, T
We assume as usual that all the 12 elements are equiprobable events and the probability of 1 ace and head is 12 since there is only one occurrence of f1, Hg among the 12 points as seen in the first row and first column in Table 2.3.1. Using Eq. (2.3.1), we have 1 P{1, H} ¼ P{1} P{H} ¼ 16 12 ¼ 12 , illustrating that in this example, functional independence coincides with statistical independence.
Example 2.3.2 Two dice, one red and the other blue, are tossed. These tosses are functionally independent, and we have the Cartesian product of 6 6 ¼ 36 elementary events in the combined sample space, where each event is equiprobable. We seek the probability that an event B defined by the sum of the numbers showing on the dice equals 9. There are 4 four points {(6,3), (5,4), (4,5), (3,6)}, and hence P{B} ¼ 36 ¼ 19. We now condition the event B with an event A defined as the red die shows odd numbers. The probability of 1 the event A is P{A} ¼ 18 36 ¼ 2. We want to determine whether the events A and B are statistically independent. These events are shown in Fig. 2.3.1, where the first number is for the red die and the second number is for the blue die. 2 1 From Fig. 2.3.1 P{A > B} ¼ P{(3,6), (5,4)} ¼ 36 ¼ 18 and ¼ P{A} P{B} ¼ 12 19 ¼ 1 18 showing statistical independence. We compute P{B j A} from the point of view of reduced sample space. The conditioning event reduces the sample space from 36 points to 18 equiprobable points and the event {B j A} ¼ {(3,6), (5,4)}. Hence 2 P{B j A} ¼ 18 ¼ 19 ¼ P{B}, or, the conditioning event has no influence on B. Here, even though the events A and B are functionally dependent, they are statistically independent. However, if another set C is defined by the sum being equal to 8 as shown in 5 Fig. 2.3.2, then P{C} ¼ 36 . Here the events C and A are not statistically independent because P{C} P{A} ¼ 5 1 5 2 4 36 2 ¼ 72 = P{C > A} ¼ P{(3,5), (5,3)} ¼ 18 ¼ 72. In this example, we have the case where the events A and C are neither statistically independent nor functionally independent.
FIGURE 2.3.1
20
Probability Space and Axioms
FIGURE 2.3.2
B 2.4
TOTAL PROBABILITY AND BAYES’ THEOREM
Let {Ai , i ¼ 1, . . . , n} be a partition of the sample space and let B an arbitrary event in the sample space S as shown in Fig. 2.4.1. We will determine the total probability PfBg, given the conditional probabilities P{B j A1 }, P{B j A2 }, . . . , P{B j An }. The event B can be given in terms of the partition as B ¼ {B > S} ¼ B > {A1 < A1 < < An } ¼ {B > A1 } < {B > A2 } < < {B > An }
(2:4:1)
The events {B > A1 }, {B > A2 }, . . . , {B > An } in Eq. (2.4.1) are mutually exclusive since A1, A2, . . . , An are mutually exclusive and we have from Kolmogorov’s axiom 3a: P{B} ¼ P{B > A1 } þ P{B > A2 } þ þ P{B > An }
(2:4:2)
Using the definition of conditional probability from Eq. (2.2.1), we can rewrite Eq. (2.4.2) as P{B} ¼ P{B j A1 }P{A1 } þ P{B j A2 }P{A2 } þ þ P{B j An }P{An } ¼
n X
P{B j Ai }P{Ai }
(2:4:3)
i¼1
Equation (2.4.3) is the expression for the total probability P{B} in terms of the conditional probabilities P{B j Ai , i ¼ 1, . . . , n}. We shall give an example to illustrate this concept.
FIGURE 2.4.1
2.4 Total Probability and Bayes’ Theorem
21
Example 2.4.1 Four computer companies A, B, C, D supply microprocessors to a company. From previous experience it is known that the probability of the processor being bad if it comes from A is 4%, B is 2%, C is 5%, and D is 1%. The probabilities of picking supplier A is 20%, B is 30%, C is 10%, and D are 40%. We want to find the probability that a processor chosen at random is bad. Let us define X as the event that the processor is bad. We need to find P{X} from the conditional probabilities P{X j A} ¼ 0:04, P{X j B} ¼ 0:02, P{X j C} ¼ 0:05, and P{X j D} ¼ 0:01. We are also given that P{A} ¼ 0:2, P{B} ¼ 0:3, P{C} ¼ 0:1, and P{D} ¼ 0:4. Substituting these quantities in Eq. (2.4.3), we obtain P{X} ¼ P{X j A}P{A} þ P{X j B}P{B} þ P{X j C}P{C} þ P{X j D}P{D} ¼ 0:04 0:2 þ 0:02 0:3 þ 0:05 0:1 þ 0:01 0:4 ¼ 0:023 Thus the probability that the processor is bad is 2.3%.
Bayes’ Theorem
The next logical question to ask is, “Given that the event B has occurred, what are the probabilities that A1, A2, . . . , An are involved in it?” In other words, given the conditional probabilities P{B j A1 }, P{B j A2 }, . . . , P{B j An }, we have to find the reverse conditional probabilities P{A1 j B}, P{A2 j B}, . . . , P{An j B}. Using the total probability P{B} from Eq. (2.4.3), we can write P{Ai j B} ¼
P{Ai B} P{B j Ai }P{Ai } P{B j Ai }P{Ai } ¼ ¼ Pn P{B} P{B} j¼1 P{B j A j }P{A j }
(2:4:4)
This is the celebrated Bayes’ theorem. This theorem connects the a priori probability P{Ai } to the a posteriori probability P{Ai j B}. This useful theorem has wide applicability in communications and medical imaging, as we will see in Chapter 23. Example 2.4.2 We will now continue Example 2.4.1 and find the probability that the processor came from companies A, B, C, or D given that the processor is bad. Using the result from Example 2.4.1 that P{X} ¼ 0:023, we can write from Eq. (2.4.4) P{X j A} ¼
P{X j A}P{A} 0:04 0:2 ¼ ¼ 0:348 P{X} 0:023
P{X j B} ¼
P{X j B}P{B} 0:02 0:3 ¼ ¼ 0:261 P{X} 0:023
P{X j C} ¼
P{X j C}P{C} 0:05 0:1 ¼ ¼ 0:217 P{X} 0:023
P{X j D} ¼
P{X j D}P{D} 0:01 0:4 ¼ ¼ 0:174 P{X} 0:023
Note that the sum of the resulting probabilities, namely, 0.348 þ 0.261 þ 0.217 þ 0.174 ¼ 1, as it should be because the bad processor has to come only from any one of the four companies A, B, C, or D resulting in the probability being 1. Example 2.4.3 A communication receiver sends numbers 1,2,3. As a result of inherent noise in the channel, the conditional probabilities of receiving 1,2,3 under the condition that 1,2,3 were sent are shown in Fig. 2.4.2.
22
Probability Space and Axioms
FIGURE 2.4.2
Transition Probabilities: P{1R j 1S } ¼ 0:60
P{2R j 1S } ¼ 0:25
P{3R j 1S } ¼ 0:15
P{1R j 2S } ¼ 0:35
P{2R j 2S } ¼ 0:55
P{3R j 2S } ¼ 0:10
P{1R j 3S } ¼ 0:05
P{2R j 3S } ¼ 0:30
P{3R j 3S } ¼ 0:65
The total probability that 1 was received is obtained from Eq. (2.4.3): P{1R } ¼ P{1R j 1S }P{1S } þ P{1R j 2S }P{2S } þ P{1R j 3S }P{3S } ¼ 0:6 0:25 þ 0:35 0:3 þ 0:05 0:45 ¼ 0:2775 From Bayes’ theorem, Eq. (2.4.4), the a posteriori probabilities of 1, 2, and 3 being sent conditioned on 1 received are found as follows: P{1S j 1R } ¼
P{1R j 1S }P{1S } 0:6 0:25 ¼ 0:5405: ¼ P{1R } 0:2775
P{1S } ¼ 0:25
P{2S j 1R } ¼
P{1R j 2S }P{2S } 0:35 0:3 ¼ 0:3784: ¼ P{1R } 0:2775
P{2S } ¼ 0:3
P{3S j 1R } ¼
P{1R j 3S }P{3S } 0:05 0:45 ¼ 0:0811: ¼ P{1R } 0:2775
P{3S } ¼ 0:45
The sum of these probabilities is equal to 1, as it should be. Note that the a priori probability that 3 was sent is 0.45 and after 1 has been received, the a posteriori probability is 0.0811, a very small probability. Similar posterior probabilities of 1, 2, and 3 being sent conditioned on 2 and 3 received can be found. Example 2.4.4 (Monty Hall Problem) This classic problem is called the “Monty Hall problem” because of the game show host Monty Hall who designed this game. There are three doors A, B, and C, and behind one of them is a car and behind the other two are goats. The contestant is asked to select any door, and the host Monty Hall opens one of the other two doors and reveals a goat. He then offers the choice to the contestant of switching to the other unopened door or keep the original door. The question now is whether the
2.4 Total Probability and Bayes’ Theorem
23
probability of winning the car is improved if she switches or it is immaterial whether she switches or not. The answer is counterintuitive in the sense that one may be misled into thinking that it is immaterial whether one switches or not. We will analyze this problem in two different ways. Mathematical Analysis. Let us analyze this problem from the point of view of Bayes’ theorem. We shall first assume that the host has prior knowledge of the door behind which the car is located. Let us also assume that the host opens door B given that the contestant’s choice is door A. The a priori probabilities of a car behind the doors A, B, and C are 1 1 1 P{A} ¼ ; P{B} ¼ ; P{C} ¼ 3 3 3 We can make the following observations. If the contestant’s choice is door A and the car is behind door A, then the host will open the door B with probability of 12 (since she has a choice between B and C ). On the other hand, if the car is behind door B, then there is zero probability that she will open door B because of her prior knowledge. If the car is behind door C, then she will open door B with probability 1. Thus we can write the following conditional probabilities for the host opening door B: 1 P{B j A} ¼ ; P{B j B} ¼ 0; P{B j C} ¼ 1 2 We can now calculate the total probability P(B) of the host opening door B. P{B} ¼ P{B j A}P{A} þ P{B j B}P{B} þ P{B j C}P{C} ¼
1 1 1 1 1 þ0 þ1 ¼ 2 3 3 3 2
Using Bayes’ theorem [Eq. (2.4.4)] we can now find the a posteriori probabilities of the car behind the doors A or C (B has already been opened by the host) conditioned on B: 1 1 P{BA} P{B j A}P{A} 2 3 1 P{A j B} ¼ ¼ ¼ ¼ 1 P{B} P{B} 3 2 1 P{BC} P{B j C}P{C} 1 3 2 P{C j B} ¼ ¼ ¼ ¼ 1 P{B} P{B} 3 2 Similar analysis holds for other cases of opening the doors A or C. We can see from the result above that the contestant must switch if she wants to double her probability of winning the car. TABLE 2.4.1 First Choice
Host Opens
Second Choice
Probability
A
B
A
P{B} ¼ 12 13 ¼ 16
A
C
A
P{C} ¼ 12 13 ¼ 16
B
C
B
P{C} ¼ 1 13 ¼ 13
C
B
B
P{B} ¼ 1 13 ¼ 13
24
Probability Space and Axioms
TABLE 2.4.2 First Choice A A B C
TABLE 2.4.3 First Choice A A B C
For not switching Host Opens
Second Choice
B C C B
A A B B
Win or Loss
Probability P{B} ¼ 12 13 ¼ 16 P{C} ¼ 12 13 ¼ 16 P{C} ¼ 1 13 ¼ 13 P{B} ¼ 1 13 ¼ 13
Win Win Loss Loss
For switching Host Opens
Second Choice
B C C B
C B A A
Win or Loss
Probability P{B} ¼ 12 13 ¼ 16 P{C} ¼ 12 13 ¼ 16 P{C} ¼ 1 13 ¼ 13 P{B} ¼ 1 13 ¼ 13
Loss Loss Win Win
Heuristic Analysis. In this case we shall enumerate all the possibilities and determine which possibility gives a better chance. Assume that the car is behind door A. The probabilities of A,B,C are once again 1 P{A} ¼ ; 3
1 P{B} ¼ ; 3
P{C} ¼
1 3
Conditioned on the contestant choosing door A, the host has two equiprobable choices of opening either door B or door C. However, conditioned on the contestant choosing B or C, the host has only one choice of either opening C or B. Thus 1 P{B j A} ¼ ; 2
1 P{C j A} ¼ ; 2
P{C j B} ¼ 1;
P{B j C} ¼ 1
From these conditional probabilities we can evaluate the unconditional probabilities as shown in Table 2.4.1. We can now enumerate the various win or loss possibilities for not switching and for switching in Tables 2.4.2 and 2.4.3. From the Table 2.4.2 we can see that the probability of winning the car by not switching is 16 þ 16 ¼ 13. From Table 2.4.3 we can see that the probability of winning the car by switching is 13 þ 13 ¼ 23. Thus the probability of winning the car doubles with switching.
CHAPTER
3
Basic Combinatorics
B 3.1
BASIC COUNTING PRINCIPLES
Combinatorics is the study of arrangement and manipulation of mathematical elements in sets and essentially involves counting. We will discuss two basic counting principles. The Addition Rule
A task A consists of m subtasks A1, A2, . . . , Am that can be done in n(A1), n(A2), . . . , n(Am) ways, respectively. If no two of them can be done at the same time, then the total number of ways n(A) of doing the task A is n(A) ¼ n(A1 ) þ n(A2 ) þ þ n(Am )
(3:1:1)
This rule is the same as in set theory [Eq. (1.2.5a)] where the cardinality of the union of mutually exclusive sets is the sum of the cardinalities of the individual sets. Thus, the addition rule is a restatement of one of the properties of mutually exclusive sets. Example 3.1.1 An engineering student undertaking a capstone project T has a choice of 4 professors A,B,C,D who can give him a list of projects from which he can choose one. A has 5 projects, B has 7 projects, C has 6 projects, and D has 2 projects. Since the student can not take any other project once he has chosen a project, the number of choices he has is n(T) ¼ n(A) þ n(B) þ n(C) þ n(D) ¼ 5 þ 7 þ 6 þ 2 ¼ 20 The Multiplication Rule
A task A consists of m sequential subtasks A1, A2, . . . , Am that can be performed only after the previous one is completed. The numbers of ways n(A1), n(A2), . . . , n(Am) of performing the subtasks are functionally independent; that is, n(A2) ways are needed for the second step regardless of n(A1) of the first step. The number of ways n(A) of performing the task A is n(A) ¼ n(A1 ) n(A2 ) n(Am )
(3:1:2)
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
25
26
Basic Combinatorics
This is very similar to the Cartesian product in set theory, where the cardinality of the product is the product of the cardinalities of the individual sets. These sets are also functionally independent. They may or may not be statistically independent. Example 3.1.2 A professor assigns a capstone project T to senior engineering students consisting of four parts A,B,C,D that must be done in sequence independent of the previous ones. Part A can be done in 2 ways, part B in 3 ways, part C in 5 ways, and part D in 4 ways. Since A,B,C,D are functionally independent, the project can be done in n(T) ¼ n(A) n(B) n(C) n(D) ¼ 2 3 5 4 ¼ 120 ways
B 3.2
PERMUTATIONS
Many problems in combinatorics can be modeled as placing r balls into n cells. The two ways of considering permutations are (1) sampling with replacement and with ordering and (2) sampling without replacement and with ordering. Sampling with Replacement and with Ordering
We consider placing r balls into n cells with replacement and with ordering. The first ball can be placed in n cells in n ways. The second ball can be placed also in n ways. Since the placing of the second ball is functionally independent of that of the first ball, there are now n 2 ordered 2-permutations. Thus, for placing r balls into n cells, there will be nr ways of placing r balls into n cells with replacement and ordering: Number of distinct ordered r-tuples ¼ nr
(3:2:1)
The probability of each one of these r-tuples assuming that they are equally likely is n – r. Clearly we can place 100 balls into one cell or 1 ball in 100 cells. Hence in Eq. (3.2.1) it does not matter whether r . n, r ¼ n, or r , n. Example 3.2.1 The number of distinct ordered ways we can draw 2 balls from an urn containing 4 balls numbered from 1 to 4 is obtained as follows. The cells correspond to n ¼ 4, the total number of balls. The balls correspond to r ¼ 2. The number of ways 2 balls can be placed into 4 cells is, from Eq. (3.2.1), 42 ¼ 16 ways. These 16 ordered ways are shown in Table 3.2.1 for clarity.
Sampling without Replacement and with Ordering
We now consider placing r balls into n cells without replacement and with ordering. This situation is different from the previous case. The first ball can be placed in n cells in
TABLE 3.2.1 1,1 3,1
1,2 3,2
1,3 3,3
1,4 3,4
2,1 4,1
2,2 4,2
2,3 4,3
2,4 4,4
3.2 Permutations
27
n different ways. Since one of the cells is occupied, the second ball can be placed only in (n – 1) ways. The rth ball can be placed in (n – r þ 1) ways. Since placements of the balls are again functionally independent, we have Number of distinct r-tuples ¼ n(n 1) (n r þ 1)
(3:2:2)
These will be called r-permutations and will be defined by (n)r or nPr. Using the factorial notation, we can write (n)r ¼
n! (n r)!
(3:2:3)
We cannot place 5 balls into 2 cells without replacement because at least some cells will have more than 1 ball, violating the nonreplacement condition. Hence we define (n)r ¼ 0 whenever r . n. If r ¼ n, then (n)r ¼ (n)n ¼ n!. Example 3.2.2 We continue Example 3.2.1 and now draw 2 balls from 4 balls without replacement and with ordering. The number of ways this can be done is obtained by applying Eq. (3.2.3) with n ¼ 4 and r ¼ 2 which is equal to 4.3 ¼ 12 ways instead of the 16 ways that we obtained in Table 3.2.1. The 12 ways are shown in Table 3.2.2. In Table 3.2.2, the arrangement with ordering, (1,2), (1,3), (1,4), (2,3), (2,4), (3,4) represents an ordering different from (2,1), (3,1), (4,1), (3,2), (4,2), (4,3). We can now summarize the preceding results as follows. The number of different ways in which r balls can be placed into n cells with ordering and replacement is n r, and with ordering and without replacement is (n)r. Example 3.2.3 In the Olympic 100-m dash, there are 9 runners. In how many ways can the gold, silver, and bronze can be given to these runners? Here the 9 runners are the cells n and the 3 medals are the balls r. Hence, from Eq. (3.2.3), the number of ways that the medals can be distributed is (9)3 ¼ 9 8 7 ¼ 504 ways. Example 3.2.4 (Birthdays Problem) We want to determine the probability that in a class size of r two or more birth dates match. We shall assume that the year consists of 365 days and the year of birth is inconsequential. Here the 365 days in a year correspond to n cells and the class size, to r. If the class size is over 365, then two or more birth dates will match with probability 1. The number of ways r students can be placed into 365 birth dates with replacement is 365r. The number of ways they can be placed without replacement is (365)r, meaning that all the birth dates are different. Thus, the probability of no two birth dates match is P{no birthdays match} ¼
(365)r 365r
TABLE 3.2.2 1,2 3,1
1,3 3,2
1,4 3,4
2,1 4,1
2,3 4,2
2,4 4,3
28
Basic Combinatorics
FIGURE 3.2.1
TABLE 3.2.3 #
Probability
#
Probability
#
Probability
20 21 22 23 24 25 26
0.411438 0.443668 0.475695 0.507297 0.538344 0.5687 0.598241
30 31 32 33 34 35 36
0.706316 0.730455 0.753348 0.774972 0.795317 0.814383 0.832182
44 45 46 46 48 49 50
0.932885 0.940976 0.948253 0.954774 0.960598 0.96578 0.970374
The probability P(r) that two or more birth dates match is the complement of the preceding equation and is given by P{r} ¼ 1
(365)r 365r
P(r) is graphed for r ¼ 0– 50 in Fig. 3.2.1 and tabulated for r ¼ 20– 26, 30– 36, and 44 – 50 in Table 3.2.3. Note that for two or more birthdays to match, there is about 50% chance for 23 students, 71% chance for 30 students, and 95% chance for 46 students. In other words, we need only 46 students for 2 or more birthdays to match with 95% probability, but to get to 100% probability, we need to go to 366 students!
B 3.3
COMBINATIONS
Similar to the case of permutations we have two ways of considering combinations: 1. Sampling without replacement and without ordering 2. Sampling with replacement and without ordering
3.3 Combinations
29
Sampling without Replacement and without Ordering
We now discuss the case where we want to obtain a sample of r balls from n balls without replacement and without ordering. We have already seen that the number of ways to get r balls from n balls with ordering is (n)r. If we disregard ordering, then the number of ways is called the r-combination from the n balls. We can define this quantity as n {r-combination from n balls} ¼ (3:3:1) r n By ordering the elements in each r-combination, we can obtain from n r the rpermutations (n)r. This can be done in r! ways. Hence we can determine r from either of the following formulas: n r! ¼ (n)r r (3:3:2) n (n)r n! ¼ ¼ r!(n r)! r! r The quantity nr is also called the binomial coefficient since it occurs as the coefficient of the binomial expansion, given by n n n n1 n nr r n n n (a þ b) ¼ a þ a b þ þ a b þ þ b 0 1 r n Properties of Binomial Coefficients
n n ¼ nr r
1.
(3:3:3)
Expanding the righthand side of Eq. (3.3.3), we obtain n! n! n n ¼ ¼ ¼ r nr (n r)!½n (n r)! (n r)!r! 2. Pascal’s Identity:
nþ1 r
¼
n r1
þ
n r
(3:3:4)
Expanding the righthand side of Eq. (3.3.4), we obtain
n r1
þ
n r
n! n! þ (r 1)!(n r þ 1)! r!(n r)! n! r þ1 ¼ r!(n r)! n r þ 1 nþ1 n! nþ1 (n þ 1)! ¼ ¼ ¼ r!(n r)! n r þ 1 r!(n r þ 1)! r ¼
Pascal’s identity shows that if two adjacent binomial coefficients are added, the binomial coefficient in the next row in between these two coefficients results, as shown in Fig. 3.3.1. It is called the Pascal triangle.
30
Basic Combinatorics
FIGURE 3.3.1
3. Sum of Binomial Coefficients: n X n r¼0
r
¼ 2n
(3:3:5)
Substituting in the binomial expansion (1 þ x)n ¼ 1 þ
n 2 n n n x þ þ xþ x 2 1 n
x ¼ 1, we obtain Eq. (3.3.5). 4. Vandermonde’s Identity:
nþm r
r X n m ¼ k rk k¼0
(3:3:6)
Let us assume that there are two urns with n balls in the first and m balls in the second. nþm The total number of ways of picking r balls from both of these urns is r . We can also choose these r balls by first picking k balls from the first urn and (r 2 k) balls from the second urn. Since thechoice m is functionally independent, the number of ways of accomplishing this is nk rk . The realizations for every 0 k r are mutually exclusive, and hence these numbers add, resulting in Eq. (3.3.6). This is shown in Fig. 3.3.2.
FIGURE 3.3.2
3.3 Combinations
31
Example 3.3.1 We continue Example 3.2.2 with sampling 2 balls from 4 balls without replacement and without ordering. Thus means that there is no distinction between (12) and (21), (23) and (32), (34), and (43). Thus we have only 6 ways and this is obtained by applying Eq. (3.3.1) with n ¼ 4 and r ¼ 2, yielding (4 3)/(2 1) ¼ 6. These are shown in Table 3.3.1. TABLE 3.3.1 1,2 2,3
1,3 2,4
1,4 3,4
Example 3.3.2 1. A committee of 3 women and 4 men must be formed from a group of 6 women and 8 men. The number of ways 3 women can be chosen from 6 women is 63 from Eq. (3.2.2) and the number of ways 4 men can be chosen from 8 men is 8 4 . Since these choices are functionally independent, the total number of ways the committee can be formed is 63 84 ¼ 1400. 2. We modify the problem above by having 2 particular men be always on the committee. In this case we have a choice of only 2 men from the 6 men after taking out the 2 required men. Thus, there are 63 62 ¼ 300 ways. 3. We again modify the problem by requiring that two particular women should not be on the committee. In this case, we calculate the number of ways that the two women will be together and subtract it from the total number of ways. There are 51 84 ¼ 350 ways that the two women will be together, and hence there are 63 84 51 84 ¼ 1400 350 ¼ 1050 ways in which two given women will not be together. Example 3.3.3 (Catalan Numbers) There are n objects to be placed in computer stacks in the following way. These objects are first stored and deleted later with the result that when all the n objects are deleted, the stack is empty. Hence, the operation store (S ) has to precede the operation delete (D), and at any point along the stack the number of stores should always be more than or equal to the number of deletes. Finally, the stack should begin with store and end with delete. We need to find the number C(n) of operations required for n objects. For example, if there are n ¼ 3 objects, the number of ways of ordering the store and delete operations are 2 3 ¼ 6 given by SSSDDD, SDSDSD, SSDDSD, SDSSDD, SSDSDD, SDSDSSD. Thus, the operation starts with an S and ends in a D, and at no point along the chain can the number of D operations exceed the S operations. We will show that the number C(n) is given by C(n) ¼
1 (2n)! n þ 1 n!n!
Since there are a total of 2n operations, the total number of ways that we can obtain an n-combination is 2n n . Out of these combinations there will be many realizations in which the number of Ds will exceed the number of Ss. We will now find the number of ways in which the Ds will exceed the Ss. If we are at the point (n þ 1)
32
Basic Combinatorics
TABLE 3.3.2 #
Stacks
#
Stacks
#
Stacks
1 2 3 4 5 6 7
1 2 5 14 42 132 429
8 9 10 11 12 13 14
1430 4862 16796 58786 208012 742900 2674440
15 16 17 18 19 20
9694845 35357670 129644790 477638700 1767263190 6564120420
2n 2n along the stack, the number of ways that D will exceed S will be nþ1 ¼ n1 . Hence 2n 2n 1 2n the required number of stacks is n n1 ¼ nþ1 n , which is the desired result. The Catalan numbers are tabulated in Table 3.3.2 for n ¼ 1 to 20.
Sampling with Replacement and without Ordering
We shall now consider forming r-combinations with replacement and without ordering. In this case we pick r balls with replacement from balls in n distinct categories such as red, blue, green, and brown. We do not distinguish among balls of the same color, and the distinction is among different colors. For example, if we are picking 6 balls from the 4 color categories, then it can be arranged as shown in Figure 3.3.3. There are now 2 red balls, 1 blue ball, no green balls, and 3 brown balls. The bar symbol | separates the four categories. The arrangement cannot start with a bar or end with a bar; hence we have 4 2 1 ¼ 3 bars. We now have to choose 6 balls from 6 balls and 3 bars without replacement and without ordering. There are then 987 9 9 ¼ ¼ ¼ 84 3 6 321 ways of picking 6 balls from 4 different categories. Generalizing this result, if there are n distinct categories of balls, and if we have to pick r balls with replacement and without regard to order, then Number of r-combinations ¼
n1þr r
(3:3:7)
This is the celebrated Bose –Einstein statistics, where the elementary particles in the same energy state are not distinguishable.
FIGURE 3.3.3
3.3 Combinations
33
Example 3.3.4 An urn contains red, blue, green, yellow, white, and black balls. In how many ways can we choose 1. 10 balls? This is a direct application of Eq. (3.3.7) with n ¼ 6, r ¼ 10 and the number of ways is 6 1 þ 10 15 15 ¼ ¼ ¼ 3003 10 10 5 2. 20 balls with least 2 colors each? If we draw 2 balls of each color, then we have drawn 12 balls, leaving us to draw 20 – 12 ¼ 8 balls to be drawn without restriction. Hence, applying Eq. (3.2.7) with n ¼ 6 and r ¼ 8, we obtain the number of ways as 13 13 61þ8 ¼ 1287 ¼ ¼ 5 8 8 3. 20 balls with no more than 3 blue balls? We first calculate the number of unrestricted ways of drawing 20 balls as 6 1 þ 20 25 ¼ ¼ 53,130 20 5 We next draw at least 4 blue balls out of 20 balls. Having drawn the 4 blue balls, the number of ways to draw the remaining 16 balls is 21 21 6 1 þ 16 ¼ 20,349 ¼ ¼ 5 16 26 Thus the number of ways of drawing 20 balls with no more than 3 blue balls is 53,130 2 20,349 ¼ 32,781. 4. 20 balls with at least 4 red balls, at least 2 green balls, at least 6 white balls, and no more than 3 brown balls? We first draw the lower bounded balls, namely, 4 þ 2 þ 6 ¼ 12, before we go to the upper-bounded brown balls. We then draw the remaining 8 balls in an unrestricted manner, and the number of ways is 61þ8 13 ¼ ¼ 1287 8 5 We have to subtract from this number the number of ways 4 brown balls can be picked from the 6 colors. This number is 61þ4 9 ¼ ¼ 126 4 4 Hence the number of ways in which all the restrictions will be satisfied is 1287 2 126 ¼ 1161. Example 3.3.5 the equation
We tackle a different kind of a problem. How many solutions are there to x1 þ x2 þ x3 þ x4 þ x5 þ x6 ¼ 24
where fxk, k ¼ 1, . . . , 6g are positive integers such that
34
Basic Combinatorics
1. xk . 2 for all k? We need xk 3 for all k. This takes up 6 3 ¼ 18 of the required total of 24, leaving us to solve the equation x1 þ x2 þ x3 þ x4 þ x5 þ x6 ¼ 6 in an unrestricted manner. Here we identify, in Eq. (3.2.7), n ¼ 6 corresponding to the number of xk values and r ¼ 6 corresponding to the righthand side of the equation. Hence the number of solutions is 11 11 61þ6 ¼ 462 ¼ ¼ 5 6 6 2. x1 . 2, x2 1, x3 . 3, x4 4, x5 . 2, and x6 2? Using x1 3, x3 4, x5 3, the number of restrictions add upto 3 þ 1 þ 4 þ 4 þ 3 þ 2 ¼ 17. This leaves 24 2 17 ¼ 7 unrestricted choices. Hence the number of solutions is 61þ7 12 12 ¼ ¼ ¼ 792 7 7 5 3. x2 , 5, x3 . 10? The number of balls that can be drawn with the restriction x3 11 corresponding to n ¼ 6 and r ¼ (24 2 11) ¼ 13 is given by 18 18 6 1 þ 13 ¼ 8568 ¼ ¼ 5 13 13 The number of draws that violate the additional restriction x2 , 5 is obtained by first finding the number of draws with the additional restriction x2 6. The number of ways corresponding to n ¼ 6 and r ¼ (24 2 11 2 6) ¼ 7 is given by 61þ7 12 12 ¼ ¼ ¼ 792 7 7 5 Hence the required result is 8568 2 792 ¼ 7776 ways. Example 3.3.6 Here is an interesting example to illustrate the versatility of the formula in Eq. (3.2.7). Four dice, each with only 4 faces marked 1,2,3,4, are tossed. We have to calculate the number of ways in which the tosses will result in exactly 1 number, 2 numbers, 3 numbers, and 4 numbers showing on the faces of the dice without regard to order and with replacement. The total number of ways is tabulated in Table 3.3.3.
TABLE 3.3.3 1,1,1,1 1,1,1,2 1,1,2,2 1,2,2,2 1,1,1,3 1,1,3,3
2,2,2,2 2,2,2,3 2,2,3,3 2,3,3,3 2,2,2,4 2,2,4,4
3,3,3,3 3,3,3,4 3,3,4,4 3,4,4,4 3,3,3,1
4,4,4,4 4,4,4,1 4,4,1,1 4,1,1,1 4,4,4,2
1,1,2,3 1,1,3,4 1,1,4,2
2,2,3,4 2,2,4,1 2,2,1,3
3,3,4,1 3,3,1,2 3,3,2,4
4,4,1,2 4,4,2,3 4,4,3,1
1,2,3,4
)
4 single digits
18 double digits
12 triple digits 1 quadruple digit
3.3 Combinations
35
We can see from Table 3.3.3 that there are 4 single digits, 18 double digits, 12 triple digits, and 1 quadruple digit totaling 35 arrangements without regard to order and with replacement. The problem can be stated as follows. Four dice each with 4 faces are tossed without regard to order and with replacement. Find the number of ways in which 1. This can be achieved. With n ¼ 4 and r ¼ 4 in Eq. (3.2.7), the number of ways is
41þ4 4
¼
7 7 ¼ 35 ¼ 3 4
which agrees with the total number in the table. 2. Exactly one digit is present. With n ¼ 4 and r ¼ 1, the number of ways is
41þ1 1
¼
4 ¼4 1
which agrees with the table. 3. Exactly 2 digits are present. The number of ways 2 digits can be drawn from 4 digits is 42 . The number of ways any particular 2 digits, for example f1,2g, will occupy a 4-digit number is a restricted case as in Example 3.2.4 with both 1 and 2 occupying two slots and the rest of the two remaining unrestricted. With n ¼ 2 and r ¼ 4 2 2 ¼ 2 in Eq. 21þ2 (3.2.7), the number of ways for this particular 3 sequence f1,2g is ¼ 2 2 ¼ 3. The total number using all the combinations are 42 32 ¼ 6 3 ¼ 18 ways. This agrees with the table. 4. Exactly 3 digits are present. The number of ways 3 digits can be drawn from 4 digits is 43 ¼ 4 ways. The number of ways 3 digits can be arranged in 4 slots such that all the three digits 3 are present is again given by the restriction 4formula 3 Eq. (3.2.7) 31þ(43) ¼ ¼ 3. Hence the total number of ways is 2 1 3 1 ¼ 12 ways, agreeing with the table. 5. Exactly 4 digits are present. The number of ways 4digits will be present is obtained from the argument in item 3 above, giving 44 41þ(4þ4) ¼1 0 Generalization
The result in Example 3.3.6 can be generalized to the tossing of n n-sided dice. The total number of sample points without regard to order and with replacement is, from Eq. (3.2.7)
n1þn n
¼
2n n
(3:3:8)
Out of these, the number of ways exactly r digits will occur can be derived as follows: The number of ways r digits can be chosen from n is nr , and if any particular sequence of r digits has to fill n slots, then the number of ways this can be done is obtained by substituting r ¼ (n 2 r) in Eq. (3.2.7), yielding
r 1 þ (n r) nr
¼
n1 nr
(3:3:9)
36
Basic Combinatorics
FIGURE 3.3.4
Since all the nr sequences are functionally independent, the number of ways N(n,r) is as follows, from Eq. (3.1.2): n1 n (3:3:10) ¼ N(n,r) ¼ nr r Using Eq. (3.3.10), N(n,r) tabulated for n ¼ 1, . . . , 8 and r ¼ 1, . . . , 8 in Fig. 3.3.4. In addition, we have the following interesting identity. Combining Eqs. (3.3.8) and (3.3.9), we have X n 2n 1 n n1 ¼ (3:3:11) n r nr r¼0 Equation (3.3.11) can also be expressed by substituting m ¼ n – 1, r ¼ n, and k ¼ r in the Vandermonde identity of Eq. (3.3.6) X X r n nþm n m nþn1 n n1 ¼ ¼ ¼ : (3:3:12) r k rk n k nk k¼0 k¼0
CHAPTER
4
Discrete Distributions
B 4.1
BERNOULLI TRIALS
We will elaborate on the Cartesian product already discussed in Chapter 1. Consider the tossing of an unfair coin with success given by fhead ¼ sg with probability p and failure given by ftail ¼ f g with probability q, and p þ q ¼ 1. The sample space is S1 ¼ fs, f g with field F ¼ ½S1 , 1, {s}, { f }. If the coin is tossed twice, then the sample space S2 ¼ fss, sf, fs, ffg is an ordered set from the Cartesian product S1 S1. The cardinality of S2 is 2 2 ¼ 4. The probability of two successes is p 2 and two failures is q 2. If we toss the coin n times, then the resulting sample space is Sn ¼ S1 S1 S1 S1 and the cardinality of Sn is 2n. The probability of n successes is p n.
Bernoulli Trials
Bernoulli trials are repeated functionally independent trials with only two events s and f for each trial. The trials are also statistically independent with the two events s and f in each trial having probabilities p and (1 2 p), respectively. These are called independent identically distributed (i.i.d.) trials. Example 4.1.1 Tossing an unfair coin 4 times constitutes 4 Bernoulli trials with heads ¼ s and tails ¼ f with Pfsg ¼ p and Pf fg ¼ 1 2 p. The sample space S is the Cartesian product with ordered 4-tuples. The cardinality of S is 24 ¼ 16 points. Example 4.1.2 Tossing a die 5 times can be considered Bernoulli trials if we define getting an ace as success, s with Pfsg ¼ 16 and not getting an ace as failure, f with Pf fg ¼ 56. The resulting sample space S is the Cartesian product with ordered quintuples with number of points equaling the cardinality 25 ¼ 32.
Bernoulli Distribution
Bernoulli distribution is the simplest of a discrete distribution. A single event is Bernoulli distributed if the probability of success is p and probability of failure is q with p þ q ¼ 1.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
37
38
Discrete Distributions
Example 4.1.3 A single die is thrown. The probability of getting an ace is p ¼ 16 and the probability of not getting an ace is q ¼ 56. Hence this experiment is Bernoulli distributed.
B 4.2
BINOMIAL DISTRIBUTION
We are given an experiment S with an event A , S with PfAg ¼ Pfsuccessg ¼ p and PfAg ¼ Pffailureg ¼ q with p þ q ¼ 1. We repeat this experiment n times, and the resulting sample space Sn is the Cartesian product Sn ¼ {S S S} n times
containing 2n elementary events. We will now determine the probability b(k; n, p) that the event A occurs exactly k times in n Bernoulli trials. We shall address this problem in two parts. In the first part we will find the probability of k successes in a particular sequence in n trials. For example, one such sequence can be {s, s, f , s, f , f , . . . , s, f } exactly k successes
For the k independent successes the probability is p k and for the (n 2 k) independent failures the probability is q n – k. Since both successes and failures are independent, we have Probability{k successes in a given sequence} ¼ pk qnk
(4:2:1)
In the second part we address the situation of k successes in any sequence in n trials. The number of ways k successes can be obtained from n trials is by sampling without replacement and without regard to order. This number from Eq. (3.3.2) is kn . These realizations are mutually exclusive since the occurrence of one realization precludes the occurrence of another. Hence the probabilities will add, giving rise to n k nk Probability{k successes in any sequence in n trials} ¼ b(k; n,p) ¼ pq (4:2:2) k This is called the binomial distribution, and b(k;n,p) is called a probability mass function. over all k. Since We will now show that this is a probability distribution by summing n n k nk q is the kth term in the binomial expansion of ( p þ q) , we can write p k n n X X n k nk p q ¼ ( p þ q)n ¼ 1 b(k; n,p) ¼ (4:2:3) k k¼0 k¼0 The probability of getting utmost r successes is given by P{number of successes r} ¼
r X k¼0
b(k; n,p) ¼
r X n k nk pq k k¼0
(4:2:4)
and the probability of getting at least r successes is given by n X n k nk P{number of successes r} ¼ pq b(k; n,p) ¼ k k¼r k¼r n X
(4:2:5)
4.2 Binomial Distribution
39
Since n! grows very rapidly with n, computational difficulties in calculating b(k;n,p) can be avoided by using the recursive formula shown below with starting value b(0;n,p). n n k p n k nk kþ1 nk1 p q b(k þ 1; n,p) ¼ ¼ pq kþ1 q k kþ1 ¼ Example 4.2.1
nk p b(k; n,p) kþ1 q
(4:2:6)
A fair die is rolled 10 times, and we will calculate
1. The probability that an ace comes up exactly 4 times. Substituting p ¼ 16 and q ¼ 56, n ¼ 10, and k ¼ 4 in Eq. (4.2.3), we obtain 10! 1 4 5 6 P{6 aces in 10 trials} ¼ ¼ 0:05427 6!4! 6 6 2. The probability that utmost 4 aces show up. In probability 1 (above) we wanted exactly 4 aces coming up. Now we are looking into the possibility of 4 or fewer aces showing up. This probability is obtained as follows: k 10k 10! 1 5 P{number of aces 4} ¼ ¼ 0:98454 k!(10 k)! 6 6 k¼0 4 X
3. The probability that the number of aces showing is between 2 and 6 inclusive. Here we have to sum b(k: 10, 16) between 2 and 6. Performing the indicated summation, we have k 10k 10! 1 5 ¼ 0:22451 P{2 number of aces 6} ¼ k!(10 k)! 6 6 k¼2 k 5 10k The graph of the probability mass function 10i ( 16 P ) ( 10 ) is i shown 10iin Fig. 4.2.1, and that of the cumulative distribution function ki¼0 10i 16 56 is shown in Fig. 4.2.2 for k ¼ 0, . . . , 10. 6 X
FIGURE 4.2.1
40
Discrete Distributions
FIGURE 4.2.2
Example 4.2.2 We shall use the recursive form [Eq. (4.2.6)] to compute the binomial coefficients in Example 4.2.1 with initial condition b(0;n,p) ¼ 0.161506. These values are substituted in Eq. (4.2.4), and the first four steps are shown: 1 b 0; 10, ¼ 0:161506 6 1 10 1 0:161506 ¼ 0:323011 ¼ b 1; 10, 6 1 5 1 9 1 b 2; 10, ¼ 0:323011 ¼ 0:29071 6 2 5 1 8 1 b 3; 10, ¼ 0:29071 ¼ 0:155045 6 3 5 The true values and the computed values are shown in Table 4.2.1, and they are the same. Example 4.2.3 Two experiments are performed. In the first experiment 6 fair dice are tossed and the person wins if she gets 1 or more aces. In the second experiment 12 dice are tossed and the person wins if she gets 2 or more aces. We want to find in which experiment the probability of winning is higher.
TABLE 4.2.1 b(k;n,p) k
Computed
Calculated
k
Computed
Calculated
0 1 2 3 4 5
0.161506 0.323012 0.290711 0.155046 0.054266 0.013024
0.161506 0.323011 0.29071 0.155045 0.054266 0.013024
6 7 8 9 10
0.002171 0.000248 0.000019 0.000001 0
0.002171 0.000248 0.000019 0.000001 0
4.3 Multinomial Distribution
41
The probability of getting one or more aces out of 6 tosses is the same as the complement of the probability of getting no aces in 6 throws. This is obtained from Eq. (4.2.5) with n ¼ 6, p ¼ 16 and q ¼ 56: 0 6 1 5 6 1 P{no aces in 6 throws} ¼ 1 ¼ 0:6651 0 6 6 The probability of getting 2 or more aces in 12 throws is the same as the complement of the sum of the probabilities of getting no aces and one ace in 12 throws. With n ¼ 12, we obtain from Eq. (4.2.5) 1 P{no aces in 12 throws} P{one ace in 12 throws} 0 12 1 11 12 12 1 5 1 5 ¼1 1 6 6 6 6 0 1 ¼ 1 0:1122 0:2692 ¼ 0:6186 Hence the probability of getting one or more aces in 6 throws is better than getting two or more aces in 12 throws. Example 4.2.4 An urn consists of 40 red balls and 60 green balls. What is the probability of getting exactly k red balls in a sample size of 10 if the sampling is done 1. With replacement? The meaning of “with replacement” is to pick a ball from the 100 in the urn note its color and replace it and pick again, and this process is continued for 10 times. Thus Pfred ballg ¼ p ¼ 25 and Pfgreen ballg ¼ q ¼ 35 and with n ¼ 10, the probability of k red balls in 10 trials is given by k 10k 2 2 3 10 b k; n, ¼ k 5 5 5 2. Without replacement? Here we sample the balls and do not replace it in the urn. 100 Thus, the number of ways of getting 10 balls from 100 balls 40 is 10 . Out of these 10 balls, the number of ways of getting kred balls is k and the number of ways of getting (10 2 k) blue balls is 1060 k . Since getting k red balls and 40 (10 602k) blue balls are functionally independent, the total number of ways are k 10 k using the product rule. Hence the probability P(k) of k red balls without replacement is given by 40 60 k 10 k P(k) ¼ 100 10
B 4.3
MULTINOMIAL DISTRIBUTION
The binomial distribution of the previous section can be generalized to n repeated independent trials where each trial has m outcomes fEi, i ¼ 1, . . . , mg with probabilities PfEig ¼ fpi, i ¼ 1, . . . , mg and p1 þ p2 þ þ pm ¼ 1
(4:3:1)
42
Discrete Distributions
Each of the outcomes fEi, i ¼ 1, . . . , mg occurs fki, i ¼ 1, . . . , mg times with k1 þ k2 þ þ km ¼ n
(4:3:2)
The probability Pfk1, k2, . . . , kmg is given by the multinomial probability distribution, n pk1 pk2 pkmm P{k1 , k2 , . . . , km } ¼ k1 , k2 , . . . , km 1 2 n! pk1 pk2 pkmm k1 !k2 !; km ! 1 2
¼
(4:3:3)
If m ¼ 2, the multinomial distribution reduces to the binomial distribution. Example 4.3.1 A pair of dice is rolled 8 times. We will find the probability that 7 or 11 occurs thrice; 2, 3, or 12 twice; any pair twice; and any other combination once. For each rolling of a pair there are four events E1, E2, E3, E4 and their probabilities are obtained from Table 2.2.1 and are shown below: E1 ¼ {7 or 11}
8 36 4 P{E2 } ¼ p2 ¼ 36 6 P{E3 } ¼ p3 ¼ 36 18 P{E4 } ¼ p4 ¼ 36 P{E1 } ¼ p1 ¼
E2 ¼ {2, 3 or 12} E3 ¼ {any pair} E4 ¼ {other combination}
In Eq. (4.3.3) we substitute k1 ¼ 3, k2 ¼ 2, k3 ¼ 2, k4¼1 with p1, p2, p3, p4 as given above, yielding 3 2 2 1 8! 8 4 6 18 P{3, 2, 2, 1} ¼ ¼ 0:003161 3!2!2!1! 36 36 36 36
B 4.4
GEOMETRIC DISTRIBUTION
Closely allied to the binomial distribution is the geometric distribution. A sequence of independent Bernoulli trials with p as the probability of success can be modeled in two different ways. The distribution of the number of successes k is binomial. The waiting time until the first success occurs is geometrically distributed. If the first success is to occur at the kth trial, then we must have (k 2 1) failures preceding the first success. Since the trials are independent, this probability is given by g(k,p) ¼ (1 p)k1 p, k ¼ 1,2, . . .
(4:4:1)
This is the geometric distribution with parameter p. Further, the waiting times between each success and the next are independent and are geometrically distributed. We can show that the sum to infinity of g(k,p) is indeed 1 as shown below: 1 X k¼1
g(k,p) ¼ p
1 X k¼1
(1 p)k1 ¼
p ¼1 1 (1 p)
4.4 Geometric Distribution
43
Example 4.4.1 In an urn there are r red balls and b blue balls. Balls are selected at random with replacement so that the first blue ball is obtained. We have to find the probability that 1. Exactly k draws are needed for the first blue ball. The probability of a blue ball is p ¼ b/(b þ r). Substituting this value p in Eq. (4.4.1), the required probability is b b k1 b g k, ¼ 1 bþr bþr bþr k1 r b br k1 ¼ ¼ bþr b þ r (b þ r)k 2. At least m draws needed for the first blue ball. The first success occurs at the mth draw after (m 2 1) failures. Since the failure probability is q ¼ [r/(b þ r)], we have from the definition of the geometric distribution this probability is [r/(b þ r)]m21. The same result can also be obtained from the cumulative summation as follows: k1 1 1 X b b X r g k, ¼ bþr b þ r k¼m b þ r k¼m # m1 " 2 b r r r þ ¼ 1þ þ bþr bþr bþr bþr 2 3 m1 m1 b r 1 r 6 7 ¼ ¼ 4 r 5 bþr bþr bþr 1 bþr This geometric distribution is shown in Fig. 4.4.1 for r ¼ 100, b ¼ 20, and k ¼ 1, . . . , 10 and the cumulative distribution in Fig. 4.4.2.
FIGURE 4.4.1
44
Discrete Distributions
FIGURE 4.4.2
B 4.5
NEGATIVE BINOMIAL DISTRIBUTION
The binomial distribution finds the probability of exactly k successes in n independent trials. The geometric distribution found the number of independent trials needed for the first success. We can generalize these two results and find the number of independent trials required for k successes. This distribution is known as the negative binomial or the Pascal distribution. Let us now define the following events to find this distribution: E ¼ n trials for exactly k successes A ¼ exactly (k 1) successes occur in (n 1) trials B ¼ nth trial results in kth success Since E ¼ A > B and A and B are independent, we have P{E} ¼ P{A} P{B}
(4:5:1)
We will now compute PfAg. In a sequence of (n 2 1) trials we have exactly (k 2 1) successes and [n 2 1 2 (k 2 1)] ¼ (n 2 k) failures. Since these trials are independent, the probability of that particular sequence is p k21q n21. There are n1 k1 mutually exclusive sequences, and hence n 1 k1 nk p q P{A} ¼ b(k 1; n 1, p) ¼ (4:5:2) k1 Since the probability PfBg ¼ p, we can substitute this and Eq. (4.5.2) in Eq. (4.5.1) and write n 1 k1 nk n 1 k nk p q nb(k; n,p) ¼ P{E} ¼ p p q , n ¼ k, k þ 1, . . . (4:5:3) ¼ k1 k1 We have to show that the sum of nb(k;n,p) from n ¼ k to 1 equals 1. Or 1 X n 1 k nk pq nb(k; n,p) ¼ ¼1 k1 n¼k n¼k
1 X
(4:5:4)
4.5
TABLE 4.5.1
Negative Binomial Distribution
45
Negative binomial
n
Probability
n
Probability
n
Probability
5 6 7 8
0.16807 0.252105 0.226894 0.158826
9 10 11 12
0.095296 0.05146 0.02573 0.01213
13 14 15
0.005458 0.002365 0.000993
By substituting (n 2 k) ¼ m, we can rewrite Eq. (4.5.4) as 1 1 1 X X kþm1 k m X kþm1 k m pq ¼ p q ¼1 nb(k; k þ m,p) ¼ k1 m m¼0 m¼0 m¼0
(4:5:5)
The Taylor series expansion of (1 2 x)2k yields (k)( k 1) (k)(k 1)(k 2) (x)2 þ (x)3 þ 2! 3! 1 X kþm1 m ¼ x m m¼0
(1 x)k ¼ 1 þ kx þ
(4:5:6)
Substituting x ¼ q in Eq. (4.5.6) and (1 2 q) ¼ p, we obtain 1 1 X X kþm1 m kþm1 k m k p ¼ q , p q , which is Eq. (4:5:5) or 1 ¼ m m m¼0 m¼0 In Eq. (4.5.3), if we substitute k ¼ 1, then we get the geometric distribution. Example 4.5.1 A computer software company wants to recruit 5 new engineers. The probability that a person interviewed is given a job offer is 0.7. We want to find the distribution of the number of people interviewed for the 5 positions. We use Eq. (4.5.3) with p ¼ 0.7 and k ¼ 5. Thus n1 P{E ¼ n} ¼ (0:7)5 (0:3)n5 , n ¼ 5, 6; . . . 51 PfE ¼ ng is shown for n ¼ 5,6, . . . ,15 in Table 4.5.1, and the probability mass function is shown in Fig. 4.5.1.
FIGURE 4.5.1
46
Discrete Distributions
From the table and the figure we see that the maximum probability of 0.2521 occurs at k ¼ 6 and it is most likely that company will interview exactly 6 persons for the 5 jobs.
B 4.6
HYPERGEOMETRIC DISTRIBUTION
The binomial distribution is obtained while sampling with replacement. Let us select a sample of n microprocessor chips from a bin of N chips. It is known that K of these chips are defective. If we sample with replacement then the probability of any defective chip is K/N. The probability that k of them will be defective in a sample size of n is given by the binomial distribution since each drawing of a chip constitutes a Bernoulli trial. On the other hand, if we sample without replacement, then the probability for the first chip is K/N, the probability for the second chip is (K 2 1)/(N 2 1), and for each succeeding sample the probability changes. Hence the drawings of the chips do not constitute Bernoulli trials and binomial distribution does not hold. Hence we have to use alternate methods to arrive at the probability of finding k defective chips in n trials. The number of sample points in this sample space is choosing n chips out of the total N chips without regard to order. This can be done in Nn ways. Out of this sample size of n, there are k defective chips and (n 2 k) good chips.The number of ways of picking k defective chips out of the total of K defective chips is Kk and the number of ways of drawing (n 2 k) good chips out of the remaining (N 2 K) good chips is NK nk . We denote the total number of ways of drawing a sample of n chips with k bad chips as the event E. Since these drawings are functionally independent the number of ways is Kk NK nk . Hence the probability of the event E is given by K NK k nk (4:6:1) P{E ¼ k} ¼ N n This is known as the hypergeometric distribution. We came across this distribution in Example 4.2.4. Since the summation over all k must be one, we must have the following in Eq. (4.6.1): n X K N K N ¼ (4:6:2) k n k n k¼0 This result follows directly from Vandermonde’s identity of Eq. (3.3.6) given by n X n m nþm ¼ (3:3:6) r k rk k¼0 with n ¼ K, m ¼ N 2 K, and r ¼ n. Example 4.6.1 (Mass Megabucks) Massachusetts megabucks lottery is played as follows. A player has to choose 6 numbers from a slate of 42 numbers. Twice a week the lottery commission draws 6 numbers. If all the 6 numbers of the player match all the 6 numbers drawn by the commission, the player wins megabucks. There are smaller prizes for matching 5, 4, or 3 numbers. We have to calculate the probability of winning megabucks or the smaller prizes.
4.6 Hypergeometric Distribution
TABLE 4.6.1
47
Mass megabucks
k
N(k)
C(k)
P(k)
0 1 2 3 4 5 6
1947792 2261952 883575 142800 9450 216 1
3 2 6 37 555 24286 5,245,786
0.371306035 0.431194105 0.168435197 0.02722185 0.001801446 0.000041176 0.000000191
Let k ¼ 0,1, . . . , 6. Substituting N ¼ 42, n ¼ 6, and K ¼ 6, we can find from Eq. (4.6.1) PfE ¼ kg: 6 42 K k 6k N(k) , k ¼ 0,1, . . . , 6 ¼ P{E ¼ k} ¼ D(k) 42 6 The results for k ¼ 0, . . . , 6 in Table 4.6.1. In this table, the second column are tabulated N(k) ¼ 6k 42K is the number of ways k digits can be formed from 6 digits. In the third 6k column C(k) ¼ D(k)/N(k) represents the chance of winning with k digits rounded to the nearest digit and in the fourth column P(k) is the probability of winning. Example 4.6.2 Hypergeometric distribution is used to estimate an unknown population from the data obtained. To estimate the population N of tigers in a wildlife sanctuary, K tigers are caught, tagged, and released. After the lapse of a few months, a new batch of n tigers is caught, and k of them are found to be tagged. It is assumed that the population of tigers does not change between the two catches. The total number of tigers N in the sanctuary is to be estimated. In Eq. (4.6.1), the known quantities are K, k, and n. The total number N has to be estimated in the following equation: K NK k nk Pk {N} ¼ N n We will find the value of N that maximizes the probability PkfNg. Such an estimate is called the maximum-likelihood estimate, which will be discussed in Chapter 18. Since the numbers are very large, we investigate the following ratio: K NK , K N1K k nk k nk Pk {N} ¼ N N1 Pk {N 1} n n N K N1 nk n Nn NK ¼ ¼ N N1K N NKnþk n nk
48
Discrete Distributions
The ratio (Pk {N})=(Pk {N 1}) is greater than 1 if (N 2 n)(N 2 K ) . N (N 2 K 2 n þ k), Nk , nK, or N , nK/k. Conclusion. If N is less than nK/k, then Pk(N) is increasing, and if N is greater than nK/k, then Pk(N ) is decreasing. Hence the maximum value of Pk(N ) occurs when N is equal to the nearest integer not exceeding nK/k. Thus, if K ¼ 100, n ¼ 50, and k ¼ 20, then the population of tigers is 100.50/20 ¼ 250. Approximation to Binomial Distribution
If N and K are very large with respect to n and k, then the hypergeometric distribution can be approximated by the binomial distribution as shown below: P{E ¼ k} ¼
K k
NK nk
N n
¼
k! (N K)! n!(N n)! k!(K k)! (n k)!(N K n þ k)! N!
¼
n! K! (N n)! (N K)! k!(n k)! (K k)! N! (N K n þ k)!
¼
n K(K 1) (K k þ 1) k N(N 1) (N k þ 1)
(N K)(N K 1) (N K n þ k þ 1) (N k)(N K 1) (N k n þ k þ 1)
(4:6:3)
Since K k and N n, Eq. (3.3.7) can be approximated as n K K K (N K)(N K) (N K) P{E ¼ k} ¼ N N N k N N N k times
(4:6:4)
Nk times
Approximating p ffi K/N and q ffi (N 2 K )/n in Eq. (3.3.8), we obtain n k nk P{E ¼ k} ffi pq k
(4:6:5)
which is a binomial distribution.
B 4.7
POISSON DISTRIBUTION
Perhaps three of the most important distributions in probability theory are (1) Gaussian, (2) Poisson, and (3) binomial. We have already discussed the binomial and other allied distributions that are characterized by discrete time (n) and discrete state, that is, the cumulative distribution function has jumps occurring only at discrete points. In the regular Poisson distribution the cumulative distribution has unit jumps at random times and not at discrete times. Thus, it is discrete in state but continuous in time.
4.7 Poisson Distribution
49
The Poisson distribution takes values 0,1, . . . k, . . . at random points with a parameter l and is given by p{k ; l} ¼ el
lk , k!
k0
(4:7:1)
We will show that this is a probability function by summing k from 0 to 1 as shown below: 1 X
p{k ; l} ¼
k¼0
1 X
el
k¼0
1 X lk lk ¼ el ¼ el el ¼ 1 k! k! k¼0
(4:7:2)
Similar to Eq. (4.2.6) we can derive a recursive form for evaluating the Poisson distribution as shown below: 1 X k¼0
p{k þ 1 ; l} ¼
1 X k¼0
el
1 lkþ1 l X lk l p{k ; l} ¼ el ¼ (k þ 1)! k þ 1 k¼0 k! k þ 1
(4:7:3)
This last equation is solved from the starting value p(0, l) ¼ e 2l. Example 4.7.1 During a certain experiment the number of a particles passing through a sensor in a 1-ms interval is 3.2. The Poisson distribution from Eq. (4.7.1) with l ¼ 3.2 can be written as p{k ; 3:2} ¼ e3:2
(3:2)k , k!
k0
This is graphed in Fig. 4.7.1. We want to find the probability that 1. Six a particles pass through the sensor in a given millisecond. Substituting l ¼ 3.2 and k ¼ 6, we obtain p{6 ; 3:2} ¼ e3:2
ð3:2Þ6 ¼ 0:06079 6!
FIGURE 4.7.1
50
Discrete Distributions
2. No more than three a particles pass through the sensor. We have to find the cumulative distribution given by 3 X
p{k ; 3:2} ¼
k¼0
3 X k¼0
e3:2
(3:2)k ¼ 0:6025 k!
Example 4.7.2 The average number of customers arriving at the service counter in a supermarket is l, and the probability of k customers arriving in the interval (0,t) minutes is given by the Poisson distribution pfk ; lg ¼ e 2l[(lt)k/k!]. In the nonoverlapping intervals (0,t1) and (t1,t), we can write the following Poisson law P{n1 customers in (0,t1 ) and n2 in (t1 ,t)} ¼ P{n1 customers in (0,t1 )} P{n2 customers in (t1 ,t)} and the two events are independent in disjoint intervals. We will find the conditional probability, Pfn1 customers in (0,t1) j (n1 þ n2) customers in (0,t)g. From the definition of conditional probability, we can write P{n1 ; (0,t1 ) j (n1 þ n2 ); (0,t)} ¼ ¼
P{½n1 ; (0,t1 )½(n1 þ n2 ); (0,t)} P{(n1 þ n2 ); (0, t)} P{½n1 ; (0,t1 )½n2 ; (t1 ,t)} P{(n1 þ n2 ); (0, t)}
elt1 (lt1 )n1 el(tt1 ) ½l(t t1 )n2 ¼ n1 ! n2 ! ¼
(lt1 )n1 ½l(t t1 )n2 (n1 þ n2 )! (lt)n1 þn2 n1 !n2 !
¼
t1n1 (t t1 )n2 (n1 þ n2 )! tn1 þn2 n1 !n2 !
,
elt (lt)n1 þn2 (n1 þ n2 )!
which yields a surprising result independent of l! Poisson distribution has been used in a number of applications such as the 1. Number of telephone calls coming into an exchange 2. Number of cars entering a highway ramp 3. Number of packets transmitted along wireless lines 4. Number of customers at the checkout counter 5. Number of misprints in a book 6. Number of photons emitted Approximation to Binomial Distribution
Poisson distribution is also used for approximating a binomial distribution with parameters (n,p) under the condition that n is large compared to k and p is small enough that the product np ¼ l is of moderate magnitude. As a rule of thumb, l may
4.7 Poisson Distribution
51
be a single digit. We shall now derive this approximation: n k nk n! pk (1 p)nk b(k;n,p) ¼ pq ¼ k!(n k)! k n(n 1) (n k þ 1) l k l nk ¼ 1 k! n n ¼
n(n 1) (n k þ 1) lk (1 l=n)n nk k! (1 l=n)k
(4:7:4)
As n becomes very large and for moderate l, the quantity (1 l=n)n can be approximated by e 2l and the quantity (1 l=n)k can be approximated as 1. Finally, as k is much smaller than n, the quantity ½n(n 1) (n k þ 1)=nk can be approximated by 1. Thus the binomial distribution b(k;n,p) can be approximated as b(k; n,p)
lk l e , k!
k0
(4:7:5)
Example 4.7.3 A computer system consists of 1000 microchips, and the probability of failure of a microchip is 0.005. We have to find the probability of k microchips failing using both the binomial distribution and its Poisson approximation for k ¼ 0,1, . . . ,15. We will also find the percentage error between the two. Using binomial distribution, k chips failing in 1000 trials given by b(k; 1000,0.005) ¼ b(k) is b(k; 1000,0:005) ¼ b(k) ¼
1000! 0:005k 0:9951000k k!(1000 k)!
The value b(k) can be solved using the recursive formula developed in Eq. (4.2.6), and the steps are 1000 k 0:005 b(k) k þ 1 0:995 1000 k 1 ¼ b(k) k þ 1 188
b(k þ 1) ¼
Using the Poisson approximation we have np ¼ l ¼ 5, and substituting in the Poisson distribution, we have k 5 5 p(k ; l) ¼ p(k ; 5) ¼ p(k) ¼ e k! Both b(k) and p(k) are shown for k ¼ 0,1, . . . , 15 in Table 4.7.1 along with the percentage error. Table 4.7.1 is also graphed in Fig. 4.7.2 for the binomial distribution and the Poisson approximation. The figure shows that the approximation is very close. The error in the approximation is also graphed in Fig. 4.7.3. From the table and the graphs we see that the approximation becomes worse as k is increased and the approximation is excellent in the vicinity of k ¼ 5 ¼ l, about 0.05 –0.25%.
52
Discrete Distributions
TABLE 4.7.1
Comparison of binomial with Poisson approximation
k
Binomial
Poisson
%Error
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0.006654 0.033437 0.083929 0.140303 0.175731 0.175908 0.14659 0.104602 0.065245 0.036138 0.017996 0.008139 0.003371 0.001287 0.000456 0.000151
0.006738 0.03369 0.084224 0.140374 0.175467 0.175467 0.146223 0.104445 0.065278 0.036266 0.018133 0.008242 0.003434 0.001321 0.000472 0.000157
21.26208 20.75577 20.352343 20.050683 0.150021 0.250272 0.250272 0.14992 20.051188 20.353762 20.758823 21.267706 21.88207 22.603907 23.435549 24.379687
FIGURE 4.7.2
FIGURE 4.7.3
4.7 Poisson Distribution
53
Example 4.7.4 (See Ref. 47) We will revisit the two or more birthdays matching problem posed in Example 3.2.4, and solve it using the Poisson approximation and compare the results. In addition, we will use the Poisson approximation to find probability of matching three or more birthdays that would have been very difficult using the exact method. Given the total numbers of persons n 365, we will find, using the Poisson approximation, the probability of two or more birthdays matching. The probability of two birth1 days matching is 365 , and this we will set equal to p. The number n of ways we can obtain a sample of two without replacement and without ordering is 2 , each of which has the 1 of matching. Hence the 2n ways constitute Bernoulli trials and same probability of 365 1 is very small and since p ¼ 365 1 n(n 1) n ¼ ¼l np ¼ 2 365 730
is less than 10, all the conditions for approximation by a Poisson distribution are satisfied. Hence
P{2 or more people share the same birthday} ¼ 1 P{no birthdays match} n(n 1) l0 ¼ 1 p(0; l) ¼ 1 exp 730 0!
and [1 2 p(0 ; l)] is shown in Table 4.7.2 for the same values of n as given in Table 3.2.3 with the corresponding percentage error shown in Table 4.7.3. The percent error curve between the true value and the Poisson approximation for values of n ¼ 1, . . . ,100 is shown in Fig. 4.7.4. An examination of the error curve reveals that the Poisson approximation underestimates the true values, and for values of n higher than 82, the error is almost zero. Even then the maximum error is only about 1.46% occurring at n ¼ 26. As mentioned before, this approximation can be extended to the case of three or more matches. Approaching, as before, the number of 3-combinations is n3 ¼ n(n1)(n2) , and 6
TABLE 4.7.2
Poisson approximation for two matches
#
Probability
#
Probability
#
Probability
20 21 22 23 24 25 26
0.0405805 0.437488 0.468938 0.500002 0.530536 0.560412 0.589513
30 31 32 33 34 35 36
0.69632 0.720282 0.743058 0.764625 0.784972 0.804097 0.82201
44 45 46 47 48 49 50
0.925113 0.933618 0.941318 0.948266 0.954517 0.960121 0.965131
54
Discrete Distributions
TABLE 4.7.3
Error in Poisson approximation
#
%Error
#
%Error
#
%Error
20 21 22 23 24 25 26
1.369162 1.397497 1.420488 1.438108 1.450358 1.457273 1.458918
30 31 32 33 34 35 36
1.415262 1.392672 1.365868 1.335125 1.300742 1.263039 1.222352
44 45 46 47 48 49 50
0.833107 0.781948 0.731364 0.681649 0.633067 0.58586 0.540238
FIGURE 4.7.4
the probability p of three matches is
1 2 365 .
Substituting in the Poisson formula we obtain
P{3 or more share the same birthday} ¼ 1 P{no 3 birthdays match} n(n 1)(n 2) l0 ¼ 1 p(0; l) ¼ 1 exp 6 3652 0! n(n 1)(n 2) ¼ 1 exp 799350
This probability is graphed in Fig. 4.7.5 for values of n ¼ 1, . . . , 150 and tabulated in Table 4.7.4 for values of n ¼ 80 – 86, 90– 96, and 104 –110. Examination of Fig. 4.7.3 and Table 4.7.4 reveals that for an even chance for at least three matches, there must be about 84 people and for a 70% chance there must be about 100 people compared to 23 and 30 for at least two matches. This approximation technique can be extended to any number of matches from 2 to 365, whereas exact computation becomes extremely cumbersome, if not impossible, for anything more than two matches.
4.8 Logarithmic Distribution (Benford’s Law)
55
FIGURE 4.7.5
TABLE 4.7.4
B 4.8 4.8.1
Poisson approximation for three matches
#
Probability
#
Probability
#
Probability
80 81 82 83 84 85 86
0.460278 0.472929 0.485593 0.498257 0.510911 0.523543 0.536141
90 91 92 93 94 95 96
0.58597 0.598231 0.610393 0.622444 0.634375 0.646176 0.657838
104 105 106 107 108 109 110
0.745102 0.755146 0.764978 0.774593 0.783987 0.793155 0.802095
LOGARITHMIC DISTRIBUTION Finite Law (Benford’s Law)
First Significant Digit
Most physical data depend on units. Examples are rainfall in inches, land area in acres, blood pressure in millimeters of mercury, stock prices in dollars, distances in miles, and volume in cubic feet. If clusters of these quantities can be described by a probability distribution P(x), then such a probability distribution must be invariant under a scale change, so that P(k x) ¼ KP(x) (4:8:1) Ð Ð where k and K are scale factors. Since P(x)dx ¼ 1, we have P(k x)dx ¼ 1=k, and hence K ¼ 1=k is the normalization factor. Thus 1 P(kx) ¼ P(x) or kP(kx) ¼ P(x) k Differentiating Eq. (4.8.2) with respect to k, we obtain P(kx) þ k
d d(kx) P(kx) ¼0 d(kx) dk
(4:8:2)
56
Discrete Distributions
Substituting k ¼ 1 in the equation above, we have d x P(x) ¼ P(x) dx The solution for Eq. (4.8.3) is P(x) ¼
1 x
(4:8:4)
Equation (4.8.4) can be considered as a probability distribution only for finite values of x since the integral for infinite values of x diverges. We shall assume that the data are defined to some arbitrary base B rather than base 10. If X1 is the random variable corresponding to the occurrence of the first digit d to the base B, then the probability of occurrence of PX1(d) is given by Ð dþ1 1 dx d PX1 (d) ¼ dÐ B ¼ Ð x ; d ¼ 1,2, . . . , B 1 B1 1 P(x)dx 1 x dx 1 ln 1 þ ln (d þ 1) ln (d) d ¼ ¼ ln (B) ln (1) ln (B) Ð dþ1
P(x)dx
(4:8:5)
We can show that indeed Eq. (4.8.5) is a probability function as follows: B1 X 1 1 1 1 ln 1 þ ¼ ln 1 þ þ ln 1 þ þ ln 1 þ d 1 2 B1 1 ¼ ln
B 1 Y 1
1 234 B 1þ ¼ ln ¼ ln (B) d 123 B 1
(4:8:6)
Thus PX1(d ) over all digits 1 to B21 sums to 1. If the base B is 10, the generic ln becomes log, and the probability of the first significant digit d from 1 to 9 is, 1 PX1 (d) ¼ log 1 þ ; d ¼ 1,2, . . . , 9 (4:8:7) d This equation is called Benford’s law, first stated in 1938 by Benford [5]. It gives the probability of occurrence of the first significant digit in a cluster of distributions. Incidentally, 0 cannot be the first significant digit. It can be a significant digit only after the first one. Example 4.8.1 In a cluster of numbers from different scale factors, we want to find the occurrence of 1,2, . . . , 9 as the first significant digit. Using Eq. (4.8.7), we have 1 PX1 (1) ¼ log 1 þ ¼ 0:30103 1 The probability of 1 as the first significant digit is 30.1% and not a uniform distribution of 11.1% as one would expect! The probability of 2 is 17.6%. The probabilities of other significant digits are tabulated in Table 4.8.1 and plotted in Fig. 4.8.1. They show that 9 as the first significant digit has the least probability of occurrence of 4.6%. Example 4.8.2 As a realistic example we have taken the values of physical constants (73 in number), in Chapter 17 of Ref. 18 and tabulated the significant digits from 1 to 9 in
4.8 Logarithmic Distribution (Benford’s Law)
TABLE 4.8.1
57
Benford distribution
d
fD(d )
1 2 3 4 5 6 7 8 9
0.30103 0.176091 0.124939 0.09691 0.079181 0.066947 0.057992 0.051153 0.045757
FIGURE 4.8.1
Table 4.8.2. The physical constants have totally different units. The probabilities of the significant digits are shown in Fig. 4.8.2 along with the graph of Benford’s law. The table and the graphs show that even for a small sample of 73, the probabilities follow the logarithmic distribution reasonably closely except for the number 3. In conclusion, in a mixed population of different scale factors, the probabilities of the digits 1 to 9 are logarithmically distributed and are not uniform—a surprising result!
TABLE 4.8.2 k
p(k)
1 2 3 4 5 6 7 8 9
0.342 0.205 0.055 0.096 0.096 0.055 0.027 0.041 0.082
58
Discrete Distributions
FIGURE 4.8.2
Second Significant Digit
We shall now compute the occurrence of 0,1, . . . , B 2 1 as the second significant digit. Note that 0 can occur as a subsequent significant digit after the first. We will only consider numbers of only first-order magnitude since the pattern will be the same for any other order. With base B and the total number of digits being B 2 1, the occurrences of 2 as the second significant digit is 1.2,2.2, . . . (B 2 1) . 2. Figure 4.8.3 shows in logarithmic scale the arrangement of numbers for base 10. Thus, the probability of 2 as the second significant digit occurring after the first digit 1 is given by an equation similar to Eq. (4.8.5): 13 ln ln (1:3) ln (1:2) 12 P{2 as second digit after first digit 1} ¼ ¼ ln (B) ln (1) ln (B)
(4:8:8)
If X2 is the random variable corresponding to the second digit, then the probability of 2 occurring after all the B 2 1 first digits is given by 13 23 (B 1)3 ln þ ln þ þ ln 12 22 (B 1)2 PX2 (2) ¼ ln (B)
(4:8:9)
Summing Eq. (4.8.10) over all the first digits d ¼ 0, . . . , B 2 1, we obtain the probability
FIGURE 4.8.3
4.8 Logarithmic Distribution (Benford’s Law)
of occurrence of the second significant digit, PX2(d ), as B1 1 X 1 PX2 (d) ¼ ln 1 þ ; d ¼ 0,1, . . . ,B 1 ln (B) k¼1 kB þ d ! B 1 Y 1 1 ln 1þ ¼ ln (B) kB þ d k¼1 It can be shown that " B1 B 1 Y X ln 1þ d¼0
k¼1
1 kB þ d
#
" ¼ ln
B 1 B 1 Y Y d¼0 k¼1
1 1þ kB þ d
59
(4:8:10)
# ¼ ln (B)
(4:8:11)
and hence, PX2(d ) is a probability function. We can extend the preceding analysis to obtain a general probability function for the (n þ 1)st significant digit after the first one digit n ¼ 0,1, . . . , B21 as shown below. The third digit after the first one can be obtained from Eq. (4.8.10). The occurrence of 3 as the third significant digit is 103, 113, . . . , 1(B 2 1)3; 203, 213, . . . , 2(B 2 1)3; . . . ; (B 2 1)03,(B 2 1)13, . . . , (B 2 1)(B 2 1)3. Thus, there are B(B 2 1) possible ways in which the digit 3 or any other digit d ¼ 0,1, . . . , B21 can occur as the third significant number. An equation analogous to Eq. (4.8.9) can be written for the probability of 3 occurring as the third significant digit: 2 1 1 BX 1 PX3 (3) ¼ ln 1 þ (4:8:12) ln (B) k¼B kB þ 3 Similar to Eq. (4.8.10), we can write the probability of any digit n occurring as the third significant digit: 2 1 1 BX 1 ln 1 þ PX3 (d) ¼ ln (B) k¼B kB þ d ! 2 BY 1 1 1 ln 1þ ¼ ; ln (B) kB þ d k¼B
d ¼ 0, . . . , B 1
(4:8:13)
We can extend Eq. (4.8.13) to all (n þ 1) significant digits after the first with n ¼ 1,2, . . . , B 2 1: Bn1 1 X 1 ln 1 þ PX(nþ1) (d) ¼ ln (B) k¼Bn1 kB þ d n ¼ 1,2, . . . , B 1;
d ¼ 0, . . . , B 1
(4:8:14)
Example 4.8.3 Using Eq. (4.8.14) with the base B ¼ 10, we will find the occurrence of the digits 0,1, . . . , 9 occurring as the first, second, . . . , tenth significant digit, noting that the probability of occurrence of 0 as the first significant digit is 0. Equation (4.8.14) becomes n 10 1 X 1 1 PX(nþ1) (d) ¼ ln 1 þ ln (10) k¼10n1 k 10 þ d n ¼ 0,1, . . . , 9;
d ¼ 0,1, . . . , 9
60
Discrete Distributions
TABLE 4.8.3
Benford distribution for 1 – 9 significant digits
d
PX1(d )
PX2(d )
PX3(d)
PX4(d )
PX5(d )
PX6(d )
PX7(d )
0 1 2 3 4 5 6 7 8 9
0 0.30103 0.176091 0.124939 0.09691 0.079181 0.066947 0.057992 0.051153 0.045757
0.119679 0.11389 0.108821 0.10433 0.100308 0.096677 0.093375 0.090352 0.08757 0.084997
0.101784 0.101376 0.100972 0.100573 0.100178 0.099788 0.099401 0.099019 0.098641 0.098267
0.100176 0.100137 0.100098 0.100059 0.100019 0.09998 0.099941 0.099902 0.099863 0.099824
0.100018 0.100014 0.10001 0.100006 0.100002 0.099998 0.099994 0.09999 0.099986 0.099982
0.100002 0.100001 0.100001 0.100001 0.1 0.1 0.099999 0.099999 0.099999 0.099998
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
FIGURE 4.8.4
The values of PX (nþ1)(d) have been tabulated in Table 4.8.3 for values of d ¼ 0, . . . , 9 and n ¼ 1, . . . , 6. It is interesting to note that the first significant digit is considerably different from uniform distribution. As the level of significance increases, the distribution tends to be more and more uniform and the significant digit is practically uniform. In fact, the third significant digit is almost a uniform distribution as shown in Fig. 4.8.4. Benford’s law is being used widely by the Internal Revenue Service for detecting fraudulent tax returns.
4.8.2
Infinite Law
We will also present two versions of a logarithmic distribution where the number of outcomes are infinite: Version 1. The first version is defined by p(k,u) ¼
uk ; k ln (1 u)
0 , u , 1, k 1
(4:8:15)
4.8 Logarithmic Distribution (Benford’s Law)
61
TABLE 4.8.4 k
P(u,k)
1 2 3 4 5 6 7 8 9 10
0.390865 0.175889 0.105534 0.071235 0.051289 0.038467 0.029675 0.023369 0.018695 0.015143
where u is the shape parameter and k are the infinite outcomes. Here u is between 0 and 1, and k is greater than or equal to 1. We will show that this is a probability distribution by summing over all k: 1 X
p(k,u) ¼
k¼1
1 X uk 1 uk ln (1 u) ¼1 ¼ ¼ ln (1 u) k ln (1 u) ln (1 u) k¼1 k k¼1
1 X
(4:8:16)
In Eq. (4.8.15), as u tends to zero, p(k,u) tends to 1 as shown below: uk kuk1 (1 u) ¼1 ¼ lim u!0 k ln (1 u) u!0 k
lim p(k,u) ¼ lim
u!0
(4:8:17)
The probability mass function (pmf) given by Eq. (4.8.15) is tabulated for k ¼ 1, . . . ,10 and u ¼ 0.9 in Table 4.8.4 and shown in Fig. 4.8.5. This logarithmic distribution is used to model the number of items purchased by a customer in a supermarket in a given amount of time.
FIGURE 4.8.5
62
Discrete Distributions
Version 2. The second version of this logarithmic distribution is obtained by substituting l ¼ (1 2 u) in Eq. (4.8.15) to obtain p(k,l) ¼
(1 l)k : 0 , l , 1, k 1 k ln (l)
(4:8:18)
Here, as l tends to 1, p(k,l) tends to 1 as shown below: (1 l)k klk1 l ¼1 ¼ lim u!0 k ln (l) u!0 k
(4:8:19)
lim p(k,l) ¼ lim
u!0
B 4.9
SUMMARY OF DISCRETE DISTRIBUTIONS
Assumptions: Pfsuccessg ¼ p; Pffailureg ¼ q; p þ q ¼ 1. Distribution Type
Definition
Bernoulli
8 < p; b(p,q) ¼ q; : 0;
Binomial
8 < n k nk pq ; k b(k; n,p) ¼ : 0;
k¼1 k¼0 otherwise
Multinomial
p(k1 ,k2 , . . . , km ) ¼ m X
ki ¼ n;
i¼1
m X
k ¼ 0,1, . . . , n otherwise
n k1 ,k2 , . . . , km
pi ¼ 1
i¼1
qk1 ; 0;
k ¼ 1, 2, . . . k0
Geometric
g(k,p) ¼
Negative binomial: Pascal
8 < n 1 k nk pq ; k1 nb( p) ¼ : 0;
Hypergeometric
pk11 pk22 pkmm
8 K NK > > > k > < n k ; p(n,k) ¼ N > > > > : n 0;
k ¼ k, k þ 1, . . . otherwise
k ¼ 0,1, . . . , n
9 > > > > =
otherwise
> > > > ;
(Continued)
4.9
Summary of Discrete Distributions
Distribution Type
Poisson
Logarithmic – Benford
63
Definition
8 <
9 k = l l p(k,l) ¼ e k! ; k ¼ 0,1, . . . : ; 0; otherwise 1 1 p(d) ¼ log 1 þ ; d ¼ 1, . . . , 10N 1 N d
Logarithmic 1—semiinfinite
p(k,u) ¼
uk ; k ln (1 u)
Logarithmic 2—semiinfinite
p(k,u) ¼
(1 u)k ; k ln (u)
0 , u , 1, k 1 0 , u , 1, k 1
CHAPTER
5
Random Variables
B 5.1
DEFINITION OF RANDOM VARIABLES
What is a random variable? An outlandish definition would be that it is neither random nor a variable! The real definition of a random variable is that it is a function X that assigns a rule of correspondence for every point j in the sample space S called the domain, a unique value X(j) on the real line R called the range. Let F be the field associated with the sample space and FX be the field associated with the real line. The random variable X induces a probability measure PX in R and hence X is a mapping of the probability space {S, F, P} to the probability space {R, FX , PX } as shown below: X: {S,F,P} ! {R, FX , PX }
(5:1:1)
Consider an event D , S [ F. The random variable X maps every point ji in the event D to points xi in the event Ix , called the image of D under X, where Ix , R [ FX . X can be a random variable only if the inverse image X 1 (Ix ) belongs to the field F of subsets of S, and hence it must be an event. The mapping and the inverse mapping are shown in Fig. 5.1.1. With this restriction we should be able to find the induced probability measure PX in terms of the probability measure P as follows: PX {Ix } ¼ P{X 1 (Ix )} ¼ P{D} ¼ P{j : X(j) [ Ix }
(5:1:2)
Since Ix belongs to the field FX , on the real line it may consist of sets of the form {1 , h x}, {x , h , 1}, {x1 , h x2 }, {h ¼ x}. Out of these sets we define Ix by Ix ¼ {1 , h x}
(5:1:3)
All the other sets {x , h , 1}, {x1 , h x2 }, {h ¼ x} can be expressed in terms of the defined Ix as follows: I x ¼ {x , h 1} Ix2 Ix1 ¼ {1 , h x2 } {1 , h x1 } ¼ {x1 , h x2 } lim {IxþDx Ix } ¼ {h ¼ x}
Dx!0þ
With the definition of Ix given by Eq. (5.1.3), we can write Eq. (5.1.2) as follows: PX {Ix } ¼ P{j : X(j) [ Ix } ¼ P{j : X(j) x}
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
64
(5:1:4)
5.1 Definition of Random Variables
65
FIGURE 5.1.1
We now define the quantity P{j : X(j) x} as the cumulative distribution function (cdf) FX (x), and rewrite Eq. (5.1.4) in abbreviated notation as P{j : X(j) [ Ix } ¼ P{X x} ¼ FX (x)
(5:1:5)
Equation (5.1.5) converts from a cumbersome set function P{X x} into a convenient point function FX(x). The value x scans the entire real line, that is, 21 , x , 1, and FX(x) must be defined for all x on the real line. Note that {X x} in Eq. (5.1.5) is meaningless unless it is defined carefully, because X is a function and a function without an argument cannot be less than a number! For example, we cannot have an exponential less than 2, unless we specify the argument of the exponential. The abbreviated notation in Eq. (5.1.5) is to be defined as the probability of the set of all points j [ S such that the number X(j) is less than or equal to the number x. The random variable X should not confused with the value x that the random variable takes. The random variable X can take any value, for example, FX (y) ¼ P{X y}. Having defined the cumulative distribution function (cdf), we now define the probability density function (pdf) as the derivative of the cdf FX(x) with respect to x: P(X x þ Dx) P(X x) Dx!1 Dx
fX ðxÞ ¼ lim
FX (x þ Dx) FX (x) d ¼ FX (x) Dx!1 Dx dx
¼ lim
(5:1:6)
The cdf FX(x) can be given in terms of the pdf fX(x) as follows: ðx FX (x) ¼
fX (j)dj
(5:1:7)
1
Whereas the distribution function FX(x) is a probability measure, the density function fX(x) is not a probability measure unless it is multiplied by the infinitesimal Dx to yield fX (x)Dx ¼ P(x , X x þ Dx)
(5:1:8)
66
Random Variables
B 5.2 DETERMINATION OF DISTRIBUTION AND DENSITY FUNCTIONS Most of the difficulty in determining distribution functions for random variables can be avoided if the following step by step procedure is followed. Step 1. Since the random variable is a mapping from the sample space S to the real line, the mapping diagram is drawn connecting every point j in the sample space to the points on the real line R. Step 2. The regions for x on the entire real line from the mapped values are determined. The regions may correspond to the different subsets of S that map to the real line R. The set of points Ix will be equal to the right closed set ð1, x or, Ix ¼ {1 , h x}. Note that whereas Ix is a right closed subset, the region of definition of x is a subset of the real line R. Step 3. On the sample space S, the probability of the set of all points that map into Ix for every region of x in step 2 is determined. Step 4. The cdf FX (x) ¼ P{X x} is graphed for all the regions of x in step 2 on the entire real line. Step 5. The derivative of FX (x) determines the density function fX (x). Example 5.2.1 A die is tossed, and the random variable X is defined by the amount won (þ) or lost (2) on the face of the die as shown in Table 5.2.1. We have to find the cdf FX(x) and the pdf fX(x). We will follow the steps to find the probability functions. Step 1. The mapping diagram (Fig. 5.2.1) is drawn with positive numbers indicating win and negative numbers indicating loss. Step 2. From the mapping diagram the regions of x are (a) x 29, (b) 29 , x 24, (c) 24 , x 5, (d) 5 , x 8, and (e) x . 8. The corresponding set Ix on the real line for all the 5 regions is Ix ¼ {1 , h x}. Step 3. We will find the all the points in the sample space that map into Ix for every region of x: (a) x 29: Since no points in S map into Ix (Fig. 5.2.1a), we have FX(x) ¼ 0. (b) 29 , x 24: In this region (Fig. 5.2.1b), only one point f3g maps into Ix. Since P{3} ¼ 16 , FX (x) ¼ 16. (c) 24 , x 5: Here the points f3,2,6g from S map into Ix (Fig. 5.2.1c). Hence FX (x) ¼ 16 þ 16 þ 16 ¼ 12. (d) 5 , x 8: In this region (Fig. 5.2.1d) the points f3,2,6,5g in S map in to Ix. Hence FX (x) ¼ 12 þ 16 ¼ 23 TABLE 5.2.1 Pips on the Die 1,4 2,6 3 5
Win or Loss $ þ8 24 29 þ5
5.2 Determination of Distribution and Density Functions
67
FIGURE 5.2.1
(e) x . 8: In this region (Fig. 5.2.1e) all six points, that is, the entire sample space S, map into Ix. Hence FX (x) ¼ 1. In terms of unit step functions we can write the cdf FX(x) as follows: 1 1 1 1 FX (x) ¼ u(x þ 9) þ u(x þ 4) þ u(x 5) þ u(x 8) 6 3 6 3 Step 4. The cdf FX(x) can be graphed as shown in Fig. 5.2.2.
68
Random Variables
FIGURE 5.2.2
Step 5. We can now find the density function fX(x) by differentiating the distribution function FX(x), bearing in mind that the differentiation of the unit step is the Dirac delta function. Performing the indicated differentiation, we obtain 1 1 1 1 fX (x) ¼ d(x þ 9) þ d(x þ 4) þ d(x 5) þ d(x 8) 6 3 6 3 Note that the sum of the strengths of the impulse function adds to 1. The pdf fX(x) is shown in Fig. 5.2.3. From Fig. 5.2.2 we can write the following probabilities: (a) P{X ¼ 4} ¼ FX (4þ) FX (4) ¼ 12 16 ¼ 13 1 1 (b) P{X 4} ¼ FX (4þ) FX (1) ¼ 2 0 ¼ 2
(c) P{5 , X 4} ¼ FX ( 4þ) FX (5) ¼ 12 16 ¼ 13 1 1 1 (d) P{4 , X 4} ¼ FX (4þ) FX (4) ¼ 2 6 ¼ 3
(e) P{5 X , 8} ¼ FX (8) FX (5þ)(Note closure on the left) ¼ 12=3 2=3 ¼ 0 The example above shows that the mapping is from a discrete sample space S to discrete points on the real line R.
FIGURE 5.2.3
5.2 Determination of Distribution and Density Functions
69
TABLE 5.2.2 Time Interval
Win or Loss ($)
6:00– 6:15 6:15– 6:45 6:45– 7:15 7:15– 8:00
þ2 24 24 þ6
Example 5.2.2 A telephone call occurs at random between 6:00 p.m. and 8:00 p.m. Depending on the interval of arrival, the win/loss table is shown in Table 5.2.2. We have to find the cdf FX(x) and the pdf fX(x). Since the phone call is uniformly distributed in 120 min, the probability of a phone call in the region (0,x] is given by x/120. We follow the steps as outlined in Example 5.2.1: Step 1. The mapping diagram is shown in Fig. 5.2.4. Note that the mapping is from the real line R to the real line R. Step 2. From the mapping diagram the regions of x are (a) x 24, (b) 24 , x 2, (c) 2 , x 6, (d) x . 6. Step 3. (a) x 24: No points map FX(x) ¼ 0. 60 ¼ 12. (b) 24 , x 2: The interval that maps is 30 min; hence FX (x) ¼ 120 75 (c) 2 , x 6: The interval that maps is 45 min; hence FX (x) ¼ 120 ¼ 58. (d) x . 6: The entire sample space of 120 min maps; hence FX (x) ¼ 1. In terms of unit step functions, the cdf FX(x) is 1 1 3 FX (x) ¼ u(x þ 4) þ u(x 2) þ u(x 6) 2 8 8 Step 4. The cdf FX(x) is graphed in Fig. 5.2.5 Step 5. The pdf fX(x) is obtained by differentiating FX(x), and the result is 1 1 3 fX (x) ¼ d(x þ 4) þ d(x 2) þ d(x 6) 2 8 8 fX(x) is graphed in Fig. 5.2.6.
FIGURE 5.2.4
70
Random Variables
FIGURE 5.2.5
FIGURE 5.2.6
In the preceding example, the sample space is continuous whereas the range space is discrete. Since the random variable takes discrete values, we have a discrete random variable. Example 5.2.3 A telephone call occurs at random between 6:00 p.m. and 8:00 p.m. We want to find the cdf FX(x) and the pdf fX(x). Here we have a continuous mapping from the real line to the real line and X is a continuous random variable. Step 1. See the mapping diagram in Fig. 5.2.7. Step 2. From the mapping diagram the regions of x are (a) x 0, (b) 0 , x 120, (c) x . 120, and Ix ¼ {1 , h x). Step 3 (a) x 0: No points in S map into Ix. Hence FX(x) ¼ 0. (b) 0 , x 120: The call is uniformly distributed in the interval (0,120], and since (0,x] is an interval mapping into Ix, we have FX(x) ¼ x/120. (c) x . 120: Here the entire sample space maps into Ix, and hence FX (x) ¼ 1. Step 4. The cdf FX (x) is graphed in Fig. 5.2.8. Step 5. The density function fX(x) is obtained by taking the derivative of FX (x), or 1 fX (x) ¼ 120 , 0 x 120. This is sketched in Fig. 5.2.9. We found the cdf and pdf for discrete random variables in Examples 5.2.1 and 5.2.2 and for continuous random variables in Example 5.2.3. We will now give an example of a mixed random variable that has both continuous and discrete components.
5.2 Determination of Distribution and Density Functions
71
FIGURE 5.2.7
FIGURE 5.2.8
FIGURE 5.2.9
Example 5.2.4 A telephone call occurs at random between 6:00 p.m. and 8:00 p.m. However, between 6:20 and to 6:40 p.m. calls accumulate and are released exactly at 6:40 p.m. with a similar situation between 7:00 and 7:20 p.m. and are released at exactly 7:20 p.m. We want to find the cdf FX(x) and the pdf fX(x). Step 1. The mapping diagram shown in Fig. 5.2.10 is similar to Fig. 5.2.7 except that there are deadspots between 6:20 and 6:40 p.m. and 7:00 and 7:20 p.m. Step 2. From the mapping diagram the regions of x are (a) x 0, (b) 0 , x 20, (c) 20 , x , 40, (d) x ¼ 40, (e) 40 , x 60, (f) 60 , x , 80, (g) x ¼ 80, (h) 80 , x 120, (i) x . 120. Step 3. (a) x 0: No points in S map into Ix; hence FX(x) ¼ 0.
72
Random Variables
FIGURE 5.2.10
(b) 0 , x 20: The call is uniformly distributed in the interval (0,120]; hence FX (x) ¼ x/120. 20 ¼ 16. (c) 20 , x , 40: There is a deadspot in this region; hence FX (x) ¼ FX (20) ¼ 120 1 (d) x ¼ 40: There is jump at this point equal to P{X ¼ 40} ¼ 6; hence FX (40) ¼ 16 þ 16 ¼ 13. (e) 40 , x 60: Here also (0,x] is an interval in Ix and we have FX (x) ¼ x=120 and FX (40) , 13. (f) 60 , x , 80: Here again we have a deadspot; and hence FX (x) ¼ FX (60) ¼ 60 1 120 ¼ 2. (g) x ¼ 80: There is again a jump at this point equal to P{X ¼ 80} ¼ 16; hence FX (80) ¼ 12 þ 16 ¼ 23. (h) 80 , x 120: Here (0,x] is an interval in Ix and we have FX (x) ¼ x=120 and FX (80) ¼ 23. (i) x . 120: The entire sample space maps into Ix; hence FX(x) ¼ 1 Step 4. The cdf FX(x) is graphed in Fig. 5.2.11.
FIGURE 5.2.11
5.3 Properties of Distribution and Density Functions
73
FIGURE 5.2.12
Step 5. The pdf fX(x) is the derivative of FX(x) and is graphed in Fig. 5.2.12. From the graph of the cdf FX(x) of Fig. 5.2.11, we can obtain the following probabilities: 1. P{X ¼ 40} ¼ P{X ¼ 80} ¼ 16. 2. P{X , 80} ¼ 12. 3. P{X 80} ¼ 23 : 4. P{40 X 80} ¼ 13 : 5. P{X ¼ 100} ¼ 0: 5 5 6. P{50 , X 100} ¼ 56 12 ¼ 12 :
B 5.3 PROPERTIES OF DISTRIBUTION AND DENSITY FUNCTIONS Cumulative Distribution Function FX(x)
1. From the three examples given in Section 5.2 the cdf of a discrete random variable is a staircase function with jumps. 2. FX(x) 0, that is, nonnegative. 3. FX(21) ¼ 0, FX(1) ¼ 1. 4. If x1 , x2, then FX(x1) FX(x2), that is, FX(x) is a nondecreasing function (monotone increasing). For a continuous random variable, if x1 , x2, then FX(x1) , FX(x2), that is, FX(x) is an increasing function (strict monotone increasing). 5. FX(x) is right-continuous: FX (x) ¼ lim FX (x þ 1) 1!0
for 1 . 0
From this definition, the value that FX (x) takes at the point of a jump, is to the right of the jump, and if there is an event A in the interval {x1 , X x2 }, then the interval is closed on the righthand side so that we can write P{x1 , X x2 } ¼ P{X x2 } P{X x1 } ¼ FX (x2 ) FX (x1 ). This ensures that the point x1 is not counted twice. Figure 5.3.1 will clarify this point. ðx FX (x) ¼ fX (j)dj ¼ P{X x} (5:3:1) 6. 1
74
Random Variables
FIGURE 5.3.1
7. At the point x ¼ a of the jump ð aþ FX (x) ¼ fX (j)dj ¼ FX (aþ) FX (a) ¼ P{X ¼ a}
(5:3:2)
a
Probability Density Function fX(x)
1. fX (x) 0, nonnegative. 2. fX (x) consists of the sum of Dirac delta functions for a discrete random variable. 3. fX (x) is not a probability measure, but fX (x)Dx is a probability measure as shown: fX (x)Dx P{x , X x þ Dx}
4.
(5:3:3)
P{x , X x þ Dx} d ¼ FX (x) Dx dx
ð5:3:4Þ
fX (j)dj FX (b) FX (a) ¼ P{a , X b}
(5:3:5)
fX (x) lim
Dx!0
ðb 5. a
Probability Mass Functions (pmf’s)
We have already come across probability mass functions in connection with discrete random variables. We will now formalize this concept. The probability of a random variable equal to a number is called the probability mass function (pmf). In Eq. (5.3.2), P{X ¼ a} is the pmf. 1. P{X ¼ a} is the probability of the jump at the point x ¼ a in the cdf FX (x). Or, P{X ¼ a} ¼ FX (a) FX (a). 2. The pmf P{X ¼ a} is nonzero for discrete random variables at x ¼ a, where they jump. 3. P{X ¼ a} ¼ 0 for continuous random variables, since at any point of continuity a, FX (a) FX (a) ¼ 0. 4. The density function fX(x) for discrete random variables involves summation of the pmf’s with Dirac delta functions multiplying the pmf at every point in the range. For example, the pdf of the binomial distribution given by the
5.4 Distribution Functions from Density Functions
n
pk qnk is n X n k nk p q d(x k) fX (x) ¼ k k¼0
probability mass function
75
k
(5:3:6)
The pdf of the Poisson distribution given by the pmf el (lk =k!) is fX (x) ¼
1 X
el
k¼0
lk d(x k) k!
(5:3:7)
B 5.4 DISTRIBUTION FUNCTIONS FROM DENSITY FUNCTIONS Given any density function (pdf), the distribution function (cdf) can be found by integrating the pdf over all x on the real line as given in Eq. (5.3.1). Care must be taken in integrating over various regions along the x axis. Example 5.4.1 expressed as
We will start with a simple example. The density function fX (x) is
fX (x) ¼
8 k > > <4,
0,x2
> > :1, 2
2,x3
and is shown in Fig. 5.4.1. We have to first find k and then the distribution function FX (x) for all x. Integrating fX (x) over the range of definition of x, we have ð3 ð3 ð2 k 1 k 1 dx þ dx ¼ þ ¼ 1, [ k ¼ 1 fX (x) ¼ 2 2 0 04 22 1 1 fX (x) ¼ u(x) þ u(x 2) 4 4
FIGURE 5.4.1
76
Random Variables
FIGURE 5.4.2
We note that there are four natural ranges for x given by (1) x 0, (2) 0 , x 2, (3) 2 , x 3, (4) x . 0. We will evaluate FX(x) in all these ranges: 1. x 0: Since fX (x) is zero, FX (x) ¼ 0. 2. 0 , x 2: Using Eq. (5.3.5), we can write ðx FX (x) ¼
1 x dj ¼ 4 04
3. 2 , x 3: Again using Eq. (5.3.5) and taking care to carry over FX (2), we have ð2 FX (x) ¼
1 dj þ 04
ðx
1 1 1 1 dj ¼ þ (x 2) ¼ (x 1) 2 2 2 2 2
4. x . 3: Since fX(x) is zero in this range, FX(x) ¼ FX(3) ¼ 1. FX(x) is graphed in Fig. 5.4.2.
FIGURE 5.4.3
5.4 Distribution Functions from Density Functions
77
Example 5.4.2 This is a more involved example that characterizes a mixed random variable. The density function is given by fX (x) ¼ K{ex ½u(x 1) u(x 5) þ ex ½u(x 1) u(x 5)g 1 þ ½d(x þ 2) þ d(x) þ d(x 2) 8 (see Fig. 5.4.3). We will find K and graph the distribution function FX(x) for all x. We integrate fX(x) over the range (25,5] to evaluate the constant K. Thus ð 5 K
ex dx þ
5
1 1 ex dx þ þ ¼ 1 8 8 1
ð5
2K½e1 e5 þ
3 ¼1 8
[K¼
5 e5 )
16(e1
The ranges for x for integrating fX(x) are given by (1) x 25, (2) 25 , x 22, (3) 22 , x 21, (4) 21 , x 0, (5) 0 , x 1, (6) 1 , x 2, (7) 2 , x 5, (8) x . 5. We shall now evaluate FX(x) in each of these ranges: 1. x 25: Since fX(x) ¼ 0, we have FX(x) ¼ 0 2. 5 , x 2:
3. 2 , x 1:
4. 1 , x 0:
5. 0 , x 1:
FX (x) ¼
FX (x) ¼
FX (x) ¼
FX (x) ¼
5 16(e1 e5 Þ
ðx
ej dj ¼
5
5(ex e5 ) 1 þ 1 5 16(e e Þ 8
5(e1 e5 ) 1 7 þ ¼ 1 5 16(e e Þ 8 16
7 1 9 þ ¼ 16 8 16
FIGURE 5.4.4
5(ex e5 ) 16(e1 e5 Þ
78
Random Variables
6. 1 , x 2:
7. 2 , x 5:
8. x . 5:
FX (x) ¼
FX (x) ¼
FX (x) ¼
9 þ 16
ðx
ej dj ¼
1
9 5(e1 ex ) þ 16 16(e1 e5 )
9 5(e1 ex ) 1 þ þ 16 16(e1 e5 ) 8
9 5(e1 e5 ) 1 þ þ ¼1 16 16(e1 e5 ) 8
The graph of FX (x) is shown in Fig. 5.4.4. In the next chapter we will discuss the various continuous distributions.
CHAPTER
6
Continuous Random Variables and Basic Distributions
B 6.1
INTRODUCTION
In the last chapter we defined a continuous random variable as the mapping from a continuous sample space S to continuous points on the real line R. We also discussed some of the properties of continuous pdf fX(x) and the continuous cdf, FX(x). In this chapter we will discuss three of the basic continuous distributions: uniform, exponential, and Gaussian. Other distributions will be discussed in the next chapter.
B 6.2
UNIFORM DISTRIBUTION
The basic continuous distributions are uniform, exponential, and Gaussian. We have already seen (in Example 5.2.3) the distribution of a telephone call occurring at random in a given interval. The pdf fX(x) of a random variable uniformly distributed in the interval (a, b], (notation X U(a, b]), is given by 8 0 xa > > > < 1 (6:2:1) fX (x) ¼ a,xb > b a > > : 0 x.b and the corresponding distribution function FX(x) is 8 0 xa > > > <xa a,xb FX (x) ¼ ba > > > : 1 x.b
(6:2:2)
Such distributions occur in transmission of sinusoids where the phase angle will be uniformly distributed in (0,2p). The density function is shown in Fig. 6.2.1, and the distribution function is shown in Fig. 6.2.2. Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
79
80
Continuous Random Variables and Basic Distributions
FIGURE 6.2.1
FIGURE 6.2.2
Example 6.2.1 A sinusoidal signal with random phase angle Q, uniformly distributed between (0,2p] is sent along a communications channel. We want to find the probability that the phase angle is between (p/4, 3p/4]. Since it is uniformly distributed, the pdf is 1/2p in (0,2p], and hence the Pfp/4, 3p/ 4g is given by p 3p 1 3p p 1 P ,u ¼ ¼ 4 4 2p 4 4
B 6.3
EXPONENTIAL DISTRIBUTION
Exponential distributions find wide applicability in queuing theory, reliability theory, and communication theory. It is the continuous analog of the geometric distribution discussed in Chapter 4. The pdf of an exponential distribution is given by 0 x0 fX (x) ¼ (6:3:1) lelx x . 0 where the parameter l is the average rate at which events occur. The corresponding cdf FX(x) is given by 0 x0 FX (x) ¼ (6:3:2) 1 elx x . 0 The pdf is shown in Fig. 6.3.1, and the cdf is shown in Fig. 6.3.2 for values of l ¼ 0.5,1,2,3,4. The exponential distribution belongs to a class of distributions possessing memoryless (Markov) properties that we will presently show. A random variable X is called memoryless if it satisfies the conditional probability P{X . x þ x0 j X . x0 } ¼ P{X . x}
(6:3:3)
6.3 Exponential Distribution
81
FIGURE 6.3.1
FIGURE 6.3.2
The condition given by Eq. (6.3.3) is equivalent to P{X . x þ x0 , X . x0 } ¼ P{X . x} P{X . x0 }
(6:3:4)
or P{X . x þ x0 } ¼ P{X . x}P{X . x0 } To demonstrate the memoryless property, let X be a random variable representing the time to failure of a computer with cdf given by Eq. (6.3.2). We are given that the computer has lasted x0 hours. We want to find the conditional probability that the computer will fail at time x beyond x0 given that it has lasted for x0 hours. We proceed as follows: P{X x þ x0 j X . xo } ¼ FX (x þ x0 j X . x0 ) ¼
P{X x þ x0 , X . x0 } P{x0 , X x þ x0 } ¼ P{X . x0 } 1 FX (x0 )
¼
FX (x þ x0 ) FX (x0 ) 1 el(xþx0 ) 1 þ elx0 ¼ 1 FX (x0 ) 1 1 þ elx0
¼
elx0 (1 elx ) ¼ 1 elx , x . x0 elx0
(6:3:5)
82
Continuous Random Variables and Basic Distributions
The conditional distribution FX(x þ x0 j X . x0) is the same as FX(x)! The computer “forgets” that it has been operating for x0 hours and has no memory of previous hours of operation. Hence the exponential distribution is called a memoryless distribution.
Poisson Arrival Process
The number k of events occurring in an interval (0,t] is Poisson-distributed with p(k,l) ¼ e 2lt[(lt)k/k!], and the waiting times for the occurrence from one event to the next are independent and are exponentially distributed as e 2lt. Example 6.3.1 A computer company leases its computers. The lifetime of one of the 1 computers is exponentially distributed with l ¼ 50;000 h. If a person leases the computer for 10,000 h, we have to find the probability that the computer company will have to replace the computer before the lease runs out. By the memoryless property derived in Eq. (6.3.5), it is immaterial how long it has been leased before, and hence we have P{computer fails in t 10,000} ¼ 1 e10000l ¼ 1 e(1=5) ¼ 0:1813 and the probability that the computer will not be replaced before the lease runs out is P{lifetime . 10,000} ¼ 1 0:1813 ¼ 0:8187 However, if the failure time FX(x) is not exponentially distributed, then the probability of replacement has to be computed, taking into account the prior use of x0 hours of the computer. This is given by the conditional probability P{lifetime . x0 þ 10,000 j lifetime . 10000} ¼
1 FX (x0 þ 1000) 1 FX (x0 )
which means that we have to know x0, the number of hours the computer was operational before the lease. Hazard Rate
Given that an item has not failed upto time t, the conditional probability that it will fail in the next small interval of time Dt is determined as follows P{t , X t þ Dt j X . t} ¼ ¼
P{t , X t þ Dt, X . t} P{X . t} P{t , X t þ Dt} fX (t)Dt ¼ ¼ b(t)Dt P{X . t} 1 FX (t)
(6:3:6)
where FX(t) ¼ PfX tg represents the failure distribution and the quantity b(t) ¼ fx(t)/ [1 2 FX(t)] is called the instantaneous hazard rate. It is not a probability function as determined by the following analysis: ðt ðt ðt fX (j) dfX (x) dj ¼ ¼ ln½1 FX (t) b(j)dj ¼ 1 F 1 FX (j) (j) X 0 0 0
6.3 Exponential Distribution
83
Ð Since t0b(j)dj ! 1 as t ! 1, we conclude that b(t) is not a probability function. However, solving for FX(t), we obtain ðt ln½1 FX (t) ¼ b(j)dj (6:3:7) 0
or ðt 1 FX (t) ¼ exp b(j)dj ¼ 0 as t ! 1 0
From Eq. (6.3.7) we can obtain the cdf FX(t), given by ðt FX (t) ¼ 1 exp b(j)dj
(6:3:8)
0
Thus, b(t) uniquely determines the failure probability distribution function FX(t). While b(t) is not a probability density function, it is to be noted that the function b(x), given by 8 < fX (x) , x . t (6:3:9) b(x) ¼ 1 FX (t) : 0, xt is the conditional failure density conditioned on x . t and b(t) is the value of b(x) at x ¼ t. Example 6.3.2 Physicians are of the opinion that the death rate of a person E exercising regularly is half that of a person S who has sedentary habits. We want to compare the probabilities of survival of E and S given that the death rate of an exercising person E is bE(t) and the rate for a sedentary person S is bS(t) with bE(t) ¼ 12bS(t). Since the rates are conditional failure rates, we will assume that both persons have lived upto the age T1, and we want to compare the probabilities of their reaching the age T2 with T2 . T1. Thus we need to find P{Xi . T2 j Xi . T1 } ¼
P{Xi . T2 } 1 FXi (T2 ) ¼ P{Xi . T1 } 1 FXi (T1 )
where Xi ¼ E or S. Substituting from Eq. (6.3.8), we have ÐT ð T2 1 FXi (T2 ) exp 0 2 bXi (j)dj ¼ Ð ¼ exp bXi (j)dj , 1 FXi (T1 ) exp T1 bX (j)dj T1 0
i ¼ E or S
i
Simplifying, we obtain ð 1 T2 P{E . T2 j E . T1 } ¼ exp bS (j)dj 2 T1 ð T2 P{S . T2 j S . T1 } ¼ exp bS (j)dj T1
Thus, the probability of an exercising person who has lived upto T1 years living to T2 years is the square root of the probability of the sedentary person. If the death rate of a sedentary person, bS(t) ¼ 0.001t, T1 ¼ 60 years and T2 ¼ 70 years, then the following probabilities
84
Continuous Random Variables and Basic Distributions
can be evaluated: ð 70 P{S . 70 j S . 60} ¼ exp 0:001j dj ¼ 0:522 60
ð pffiffiffiffiffiffiffiffiffiffiffi 1 70 P{E . 70 j E . 60} ¼ exp 0:001j dj ¼ 0:522 ¼ 0:7225 2 60 The probability of a 60 year old sedentary person reaching 70 years is 0.522, whereas the same person exercising regularly will reach 70 years with a probability of 0.7225.
B 6.4
NORMAL OR GAUSSIAN DISTRIBUTION
The most important distribution in probability theory is the Gaussian distribution. Under some general conditions, a random variable X consisting of a number of components, each with a general distribution tends to a normal distribution as the number becomes very large. The probability density fX(x) of a Gaussian distribution is given by 2 1 fX (x) ¼ pffiffiffiffiffiffiffiffiffi eð1=2Þ½(xm)=s 2ps
(6:4:1)
where m is called the mean and s2 is called the variance. These quantities will be defined later. The cumulative distribution function FX(x) is given by ðx 1 pffiffiffiffiffiffiffiffiffi e(1=2)½(jm)=s dj FX (x) ¼ (6:4:2) 2ps 1 The Gaussian density function fX(x) given by Eq. (6.4.1) is shown in Fig. 6.4.1 for m ¼ 3 and s2 ¼ 4. It also shows that the area under the Gaussian pdf between (m 2 s, m þ s] is 68.3%, between (m 2 2s, m þ 2s] is 95.5%, and between (m 2 3s, m þ 3s] is 99.7%. Thus, almost the entire pdf (99.7%) is between the m + 3s points of the Gaussian curve. The Gaussian distribution function, FX(x) is shown in Fig. 6.4.2 along with (m 2 s, m þ s], (m 2 2s, m þ 2s], and (m – 3s, m þ 3s] points.
FIGURE 6.4.1
6.4 Normal or Gaussian Distribution
85
FIGURE 6.4.2
A standard Gaussian is defined as the random variable with m ¼ 0 and s2 ¼ 1, given by 1 2 fX (x) ¼ pffiffiffiffiffiffi e(1=2)x 2p
(6:4:3)
and the corresponding cumulative distribution function is denoted by FX(x), given by ðx FX (x) ¼
2 1 pffiffiffiffiffiffi e(1=2)j dj 2p 1
(6:4:4)
There is no closed-form solution for Eq. (6.4.4). Hence, FX(x) is tabulated for values of x between 25 and 5 in Appendix B. The standard Gaussian pdf fX(x) is shown in Fig. 6.4.3 with the 68.3% points between (21,1], 95.5% points between (22,2], and the 99.7% points between (23,3].
FIGURE 6.4.3
86
Continuous Random Variables and Basic Distributions
Given a random variable X with any m and s2, the random variable Z¼
X mX sX
is a standard Gaussian. Hence, the distribution function FX(x) can be given in terms of the standard Gaussian FZ (x) as follows: FX (x0 ) ¼ P{X x0 ¼} ¼ P
X mX x0 mX x0 mX ¼ FX sX sX sX
(6:4:5)
We shall now prove that the fX(x) is a probability density function by showing that if ð1 I¼
1 2 pffiffiffiffiffiffi e(1=2)x dx 2p 1
then I ¼ 1. Direct evaluation of the integral is difficult. Hence we square I and write ð1 1 (1=2)x2 1 2 pffiffiffiffiffiffi e(1=2)y dy e dx 2p 1 2p 1 ð ð 1 1 1 ð1=2Þ(x2 þy2 ) ¼ e dx dy 2p 1 1
I2 ¼
ð1
(6:4:6)
Substituting x ¼ r cos(u) and y ¼ r sin(u) in Eq. (6.4.5) we obtain, I2 ¼
1 2p
ð 2p ð 1 0
2
re(1=2)r dr du ¼ 1
(6:4:7)
0
and the result is proven. Example 6.4.1 The number of malfunctioning computers is Gaussian-distributed with m ¼ 4 and s ¼ 3. We want to find the probability that the number of bad computers is between 3 and 5. Denoting the malfunctioning computers by the random variable X, we have to find Pf3 , X 5g. Using Eq. (6.4.5) and referring to the Gaussian tables, we have
34 X4 54 1 1 P{3 , X 5} ¼ P , ¼P ,Z 3 3 3 3 3 1 1 ¼ FX FX ¼ 0:2611 3 3 Example 6.4.2 We shall study what grading on the curve in examinations means. If the average of the class in a particular examination is m, then one method of grading is to assign A to those receiving over m þ 2s, AB to those receiving between m þ s and m þ 2s, B to those receiving between m and m þ s, C to those receiving between m 2 s and m, D to those receiving between m 2 s and m 2 2s, and F to those receiving less than m 2 2s. We want to calculate the percentage of students receiving the grades A, AB, B, C, D, and F.
6.4 Normal or Gaussian Distribution
We can calculate these probabilities from Eq. (6.4.5) as follows: Xm . 2 ¼ 1 F(2) ¼ 0:0228 ¼ 2:28% A: P{X . m þ s} ¼ P s Xm 2 ¼ F(2) F(1) AB: P{m þ s , X m þ 2s} ¼ P 1 , s ¼ 0:1359 ¼ 13:59% B:
Xm 1 ¼ F(1) F(0) P{m , X m þ s} ¼ P 0 , s ¼ 0:3413 ¼ 34:13%
C:
Xm 0 ¼ F(0) F(1) P{m s , X m} ¼ P 1 , s ¼ 0:3413 ¼ 34:13%
D:
F:
Xm 1 ¼ F(1) F(2) P{m 2s , X m s} ¼ P 2 , s ¼ 0:1359 ¼ 13:59% Xm 2 ¼ F(2) ¼ 0:0228 ¼ 2:2:8% P{X m 2s} ¼ P s
These grades are shown in Fig. 6.4.4. Properties of Standard Gaussian
Density and Distribution 1. The pdf fX(x) is an even function.
pffiffiffiffiffiffi 2. fX(x) attains its maximum value of 1= 2p at x ¼ 0. Ð 3. The cdf FX(x) ¼ x21fX(j)dj.
FIGURE 6.4.4
87
88
Continuous Random Variables and Basic Distributions
4. FX (0) ¼ 1 : 2 5. The error function erf(x) is defined as 2 erf(x) ¼ pffiffiffiffi p
ðx
2
ej dj
(6:4:8)
0
6. The complement of the error function erfc(x) is defined as ð 2 1 j2 e dj ¼ 1 erf(x) erfc(x) ¼ pffiffiffiffi p x
(6:4:9)
7. The tails of the distribution Q(x) is used in describing the probability of error in transmission of communication signals. It is defined as ð 1 1 (j2 =2) Q(x) ¼ pffiffiffiffiffiffi e dj (6:4:10) 2p x We will discuss this function in greater detail in the next section. 8. Q(x) in terms of error function complement erfc(x) is 1 x Q(x) ¼ erfc pffiffiffi 2 2
(6:4:11)
9. The general distribution function FX(x) in terms of erf(x) and erfc(x) is pffiffiffiffi ðx ð 1 1 2 ðxmÞ= 2s j2 (1=2)½(jm)=s2 FX (x) ¼ pffiffiffiffiffiffiffiffiffi e dj ¼ pffiffiffiffi e dj 2 p 1 2ps 1 1 1 xm 1 xm ¼ þ erf pffiffiffiffiffiffi ¼ 1 erfc pffiffiffiffiffiffi (6:4:12) 2 2 2 2s 2s 1 10. (6:4:13) FX (m) ¼ 2
Gaussian Tails Q(x)
As mentioned in property 7 [Eq. (6.4.10)] Q(x) is used to express the bit error probability in transmission of communication signals. Since Q(x) does not have a closed-form solution, bounds can be established. The distribution Q(x) can expanded into an asymptotic series [1] given by 2 ex 1 1 1:3 n 1:3 (2n 1) p ffiffiffiffiffiffi 1 2 þ 4 þ þ (1) Q(x) ¼ (6:4:14) x x x2n 2p x and for large x, Q(x) can be approximated by 2
ex 1 Q(x) pffiffiffiffiffiffi 2p x
(6:4:15)
For small x, Q(x) diverges, and hence this is not a very good bound. A better bound is 2
ex 1 Q(x) QA(x) ¼ pffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi 2p 1 þ x2
(6:4:16)
6.4 Normal or Gaussian Distribution
89
Even this bound, although better than Eq. (6.4.15), is not very good for small values of x. However, a tighter bound has been established by Bo¨rjesson and Sundberg [8] by first integrating Q(x) by parts as shown below: ð pffiffiffiffiffiffi ð 1 1 (j2 =2) 1 1 (j2 =2) je Q(x) ¼ pffiffiffiffiffiffi e dj ¼ 1 2p dj 2p x x j ð1 1 1 1 (j2 =2) 2 ¼ pffiffiffiffiffiffi e(x =2) e dj 2 2p x x j and the expression for Q(x) can be manipulated to obtain Q(x) ¼
1
1 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi e(x =2) 2 (1 a)x þ a x þ b 2p
(6:4:17)
Substituting the values a ¼ 1 and b ¼ 1 in Eq. (6.4.17), we obtain Eq. (6.4.16). Better values for a and b can be obtained by minimizing the absolute relative error function 1(x), given by
Q0 (x) Q(x)
(6:4:18) 1(x) ¼
Q0 (x) and constraining 1(0) ¼ 0. In Eq. (6.4.18) Q0(x) is the true value given by Eq. (6.4.10). As a consequence, the upper bound QU(x) is obtained for values of a ¼ 0.33919 and b ¼ 5.533425, and the lower bound QL(x) is obtained for values of a ¼ 1/p and b ¼ 2p. Thus the upper bound is QU(x) ¼
1 1 2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi e(x =2) 2 0:66081x þ 0:33919 x þ 5:33425 2p
(6:4:19)
and the lower bound is, QL(x) ¼
1 1 2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi eðx =2Þ (1 1=p) þ 1=p x2 þ 2p 2p
(6:4:20)
The functions Q(x), QU(x), QL(x), and QA(x) are shown in Fig. 6.4.5 and tabulated in Table 6.4.1 showing the upper and lower bounds are for all x . 0. The bound QA(x) is not good for low values of x, whereas QU(x) and QL(x) practically lie on Q(x).
FIGURE 6.4.5
90
Continuous Random Variables and Basic Distributions
TABLE 6.4.1 x 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Lower Bound QL(x)
True Value Q(x)
Upper Bound QU(x)
Approx. Value QA(x)
0.5 0.30496416 0.15704991 0.06633866 0.0226461 0.00619131 0.00134729 0.00023233 0.00003164 0.0000034 0.00000029
0.5 0.30853754 0.15865525 0.0668072 0.02275013 0.00620967 0.0013499 0.00023263 0.00003167 0.0000034 0.00000029
0.5 0.3071816 0.15837866 0.06684731 0.02279147 0.00622374 0.00135302 0.00023314 0.00003173 0.0000034 0.00000029
0.39894228 0.3148968 0.17109914 0.07184344 0.02414549 0.00650985 0.00140147 0.00023974 0.00003246 0.00000347 0.00000029
The absolute percentage errors for the upper QU(x) and lower bounds QL(x) are shown in Fig. 6.4.6. Table 6.4.2 shows in addition to the upper- and lower-bound errors the absolute percentage error for the approximate value QA(x). Since these errors are large, they are not shown in Fig. 6.4.6. The errors for the approximate value are high (20.2%). The upper-bound errors are better for smaller values of x, whereas the lower-bound is better for higher values of x. Even then, these bounds clearly illustrate that the approximation for all x is excellent with the maximum error of about 1.16% occurring at low values of x. This is much better than the usual approximation QA(x), where the maximum error is 20.2% for low values. For high values of x, QU(x), QL(x), and QA(x) provide reasonably good approximation.
Properties of Q Function
1. The limit as x ! 1 is zero: lim Q(x) ¼ 0
x!1
FIGURE 6.4.6
(6:4:21)
6.4 Normal or Gaussian Distribution
TABLE 6.4.2 x 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
91
Error percentages Lower Bound
Upper Bound
Approximate Value
0 1.15816594 1.01184414 0.70133395 0.45727327 0.29559943 0.19344425 0.12922798 0.08836646 0.06185728 0.04427826
0.00000176 0.43947331 0.17433835 0.06003046 0.1816945 0.226622 0.23152986 0.21848937 0.19884525 0.1779578 0.15815582
20.21154392 2.06109849 7.8433496 7.53846047 6.13342513 4.83410996 3.82069533 3.05864395 2.48602699 2.05102577 1.71571131
2. The limit as x ! 21 is 1: lim Q(x) ¼ 1
(6:4:22)
x!1
3. The value of Q(x) at x ¼ 0 is half: Q(0) ¼
1 2
(6:4:23)
4. Q(x) is even: Q(x) ¼ Q(x) 5. Q(x) can be expressed in terms of error function as 1 x 1 x Q(x) ¼ 1 erf pffiffiffi ¼ erfc pffiffiffi , 2 2 2 2
(6:4:24)
x0
(6:4:25)
6. If F(x) is a standard Gaussian, then Q(x) ¼ 1 F(x)
(6:4:26)
7. For any Gaussian distributed random variable X with mean m and variance s2 a m P{X . a} ¼ 1 FX (a) ¼ Q (6:4:27) s
Gaussian Approximation to Binomial Distribution
In Example 4.7.3 we saw that the binomial distribution can be approximated by a Poisson distribution under the condition that n is large compared to k and p is small enough that the product np ¼ l is of moderate magnitude. On the other hand, if npq is quite large, the Poisson approximation does not fare very well. In this case we use the Gaussian approximation, which is called the DeMoivre –Laplace limit theorem, which states that, if Sn is the total number of successes in n Bernoulli trials, each with probability of success p and failure q with ( p þ q ¼ 1) with pmf b(k;n,p), then, as n ! 1, the pffiffiffiffiffiffiffiffi standardized random variable Z ¼ (Sn 2 np)/ npq converges to a standard normal distribution function where m ¼ np is the mean and s2 ¼ npq is the variance. Or, if
92
Continuous Random Variables and Basic Distributions
n1 , n2, then Sn np lim Z ¼ P n1 , pffiffiffiffiffiffiffiffi n2 ! F(n2 ) F(n1 ) n!1 npq
(6:4:28)
and the probability mass function b(k;n,p) converges to the density function fZ (z). Or lim
2 n! 1 pk qnk ! pffiffiffiffiffiffiffiffiffiffiffiffiffi e(1=2)½(xnp)=npq k)! 2pnpq
n!1 k!(n
(6:4:29)
The Demoivre –Laplace limit theorem is a special case of the central limit theorem presented in a later chapter.
Example 6.4.3 (Gaussian and Poisson Approximation) We will find the Gaussian approximation for the binomial distribution given in Example 4.7.3, where the Poisson approximation has already been found. In the binomial distribution b(k;n,p) of that example, n ¼ 1000, p ¼ 0.005 with np ¼ 5, and npq ¼ 4.975. The approximate Gaussian pdf will be given by 2 1 fX (k) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi eð1=2Þ½(k5) =4:975 b(k; 1000,0:005) 2p:4:975
For the Poisson approximation np ¼ l ¼ 5, and this is given in Example 4.7.3 as k 5 pX (k;l) ¼ pX (k;5) ¼ pX (k) ¼ e5 k! Since l ¼ 5 is a single digit and npq ¼ 4.975 is not very large, we would reasonably infer that the Poisson approximation will be better than the Gaussian. The binomial distribution with the Poisson and Gaussian approximations are shown in Fig. 6.4.7 for k ¼ 0, . . . , 15. The true value of b(5; 1000, 0.005) ¼ 0.17591, the Gaussian approximation fX(5) ¼ 0.17886 with an absolute error of 1.678% and the Poisson approximation pX(5; 5) ¼ 0.17547 with an error of 0.25%, confirming our inference that the Poisson approximation is better. Figure 6.4.7 clearly shows that the Poisson approximation follows the binomial more closely than the Gaussian.
FIGURE 6.4.7
6.4 Normal or Gaussian Distribution
93
FIGURE 6.4.8
Example 6.4.4 We shall now modify Example 6.4.3 by keeping n ¼ 1000 and changing p ¼ 0.5, yielding np ¼ l ¼ 500, and npq ¼ 250. Here both l and npq are quite large, so we can infer that the Gaussian will be a better approximation than the Poisson. The approximate Gaussian pdf is given by 2 1 fX (k) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi e(1=2)½(k500) =250 b(k; 1000,0:5) 2p:250
and the corresponding Poisson approximation is PX (k;l) ¼ PX (k;500) ¼ PX (k) ¼ e500
500k k!
The binomial distribution with the Poisson and Gaussian approximations for this case are shown in Fig. 6.4.8. TABLE 6.4.3
Comparison of binomial with Gaussian and Poisson approximations
k
Binomial
Gaussian
Error 1BG
Error 1BP
2 3 4 5 6 7 8
0.083929 0.140303 0.175731 0.175908 0.14659 0.104602 0.035245
n ¼ 1000, p ¼ 0.005, np ¼ 5, npq ¼ 4.975 0.072391 0.084224 13.74699 0.119653 0.140374 14.718176 0.161758 0.175467 7.951344 0.17886 0.175467 1.678441 0.161758 0.146223 10.347485 0.119653 0.104445 14.38896 0.072391 0.065278 10.953113
0.352343 0.50683 0.150021 0.250272 0.250272 0.14992 0.051188
497 498 499 500 501 502 503
0.024775 0.025024 0.25175 0.25225 0.025175 0.025024 0.024775
n ¼ 1000, p ¼ 0.5, np ¼ 500, npq ¼ 250 0.024781 0.017731 0.023389 0.02503 0.017803 0.02438 0.025181 0.017838 0.024978 0.025231 0.017838 0.025177 0.025181 0.017803 0.024978 0.02503 0.017732 0.02438 0.024781 0.017626 0.023389
28.431591 28.85844 29.141872 29.283306 29.283306 29.141589 28.857017
Poisson
94
Continuous Random Variables and Basic Distributions
The true value for k ¼ 500 for binomial b(500,1000,0.5) ¼ 0.025225, the Gaussian approximation fX(k) ¼ 0.025231 with an absolute error of 0.025% and the Poisson approximation pX(k) ¼ 0.017838 with an error of 29.28%. In this case, the Gaussian approximation, as expected, fares very much better than the Poisson one. The results graphed in Fig. 6.4.8 for k ¼ 400, . . . , 600 clearly show that the Gaussian approximation follows the binomial much more closely than does the Poisson, unlike the scenario described in Example 6.4.3. The percentage absolute errors for the Gaussian and Poisson approximations to the binomial are defined by
b(k;n,p) fX (k;np)
100 Gaussian: 1BG ¼
b(k;n,p)
b(k;n,p) pX (k;l)
100 Poisson: 1BP ¼
b(k;n,p) These errors are tabulated for p ¼ 0.005 and 0.5 in Table 6.4.3 in the neighborhoods of np ¼ l ¼ 5 and 500, respectively. The absolute error percentages also show that for large n and for values of moderate np and npq, the Poisson approximation is better and for large values of np and npq, the Gaussian approximation is better.
CHAPTER
7
Other Continuous Distributions
B 7.1
INTRODUCTION
We will now discuss some of the other important continuous distributions that occur in communications, signal processing, queuing, statistical inference, and other probabilistic situations. This list is by no means exhaustive. For a more complete list of distributions, refer to Web references W1 and W2 (last two entries in References list at end of the book). Most of these distributions are derivable from the three basic distributions discussed in Chapter 6.
B 7.2
TRIANGULAR DISTRIBUTION
If two independent random variables X and Y are uniformly distributed in the interval (a, b], then Z ¼ X þ Y has a triangular density defined by 8 za > a,zb > > 2 > < (b a) (7:2:1) fZ (z) ¼ (z 2b þ a) b , z 2b a > 2 > > (b a) > : 0 otherwise The density fZ (z) is shown in Fig. 7.2.1 with the corresponding uniform densities of X and Y for a ¼ 1 and b ¼ 5. The distribution function FZ (z) is given by 8 0 za > > > > 1 z a 2 > > a,zb <2 b a (7:2:2) FZ (x) ¼ 1 z þ a 2b 2 > > >1 b , z 2b a > > 2 ba > : 1 z . 2b a The distribution function FZ (z) is shown for a ¼ 1 and b ¼ 5 in Fig. 7.2.2.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
95
96
Other Continuous Distributions
FIGURE 7.2.1
FIGURE 7.2.2
B 7.3
LAPLACE DISTRIBUTION
The Laplace density, also called a double-exponential density, is used to model navigation and pilot errors, speech, and image processing. As the term suggests, double-exponential density is defined by fX (x) ¼
l ljxj e 2
l.0
(7:3:1)
The cdf FX(x) is given by 8 1 > < elx FX (x) ¼ 2 > : 1 1 elx 2
x0 (7:3:2) x.0
It is an even density function, as shown for l ¼ 0.5 in Fig. 7.3.1. The cdf FX(x) is shown in Fig. 7.3.2. An alternate form of Laplace density in terms of the variance s2 is defined by pffiffi 1 fX (x) ¼ pffiffiffi e 2(jxj=s) 2s
(7:3:3)
7.4 Erlang Distribution
97
FIGURE 7.3.1
FIGURE 7.3.2
with the corresponding cdf FX(x) given by 8 pffiffi 1 > < e 2(x=s) FX (x) 2 pffiffi > : 1 1 e 2(x=s) 2
x0 (7:3:4) x.0
where s2 is the variance of the Laplace distribution.
B 7.4
ERLANG DISTRIBUTION
We already saw that the waiting time between successive events of a Poisson arrival process is exponentially distributed with parameter l. The distribution of the waiting time for n successive independent events of a Poisson arrival process is given by the Erlang distribution with n degrees of freedom and is defined for positive integral values of n by fX (x) ¼
l(lx)n1 elx , (n 1)!
where n is an integer
(7:4:1)
98
Other Continuous Distributions
The Erlang distribution is obtained by the n-fold convolution of n independent exponential distributions. The cumulative distribution corresponding to Eq. (7.4.1) can be derived as follows. Let Xn denote the time at which the nth event occurs. Let N(x) be the number of events in the interval (0,x]. Hence the event fXn x) can occur only if the number of events occurring in (0,x] is at least n. Since N(x) is Poisson-distributed, the cdf FXn(x) can be given by P{Xn x} ¼ FXn (x) ¼ P{N(x) . n} ¼
1 X
el x
k¼n
n1 X (l x)k (l x)k ¼1 el x k! k! k¼0
(7:4:2)
We can obtain the pdf fXn(x) by differentiating Eq. (7.4.2) with respect to x. Performing the indicated differentiation, we can write ( ) n1 k X d d l x (l x) {FXn (x)} ¼ 1 e dx dx k! k¼0 ¼ el x
n1 X k(l x)k1 l k¼0
( ¼ lel x
k!
n1 X (l x)k
n1 X (l x)k k¼0
n1 X (l x)k1
k!
)
(k 1)! ( ) n1 n2 X (l x)k X (l x)k l x ¼ le k! k! k¼0 k¼0 k¼0
k!
þ lel x
k¼1
or d lel x (l x)n1 {FXn (x)} ¼ fXn (x) ¼ dx (n 1)!
(7:4:3)
and Eq. (7.4.3) is the Erlang distribution. This distribution is shown in Fig. 7.4.1 for n ¼ 10 and l ¼ 2,2.5,3,3.5,4,5. Another family of Erlang distributions is shown for l ¼ 4 and n ¼ 2,3,4,5,6 in Fig. 7.4.2.
FIGURE 7.4.1
7.5 Gamma Distribution
99
FIGURE 7.4.2
In both these cases the Erlang distributions reduce to an exponential distribution for n ¼ 1. In cases where the data may not be modeled as a simple exponential, the Erlang family provides greater flexibility.
B 7.5
GAMMA DISTRIBUTION
A gamma distribution is a generalization of the Erlang distribution where n ¼ a, which may not be an integer. It finds wide use in statistics. Gamma density is a two-parameter family with parameters a . 0, termed the shape parameter, and l . 0, called the scale parameter, and is defined as fX (x) ¼
l(lx)a1 elx , G(a)
a, l, x . 0
(7:5:1)
where G(a) is the gamma function defined by the integral ð1 G(a) ¼ xa1 ex dx, a . 0
(7:5:2)
0
Integrating Eq. (7.5.2) by parts with u ¼ x a21 and dv ¼ e 2x dx, we have 1 ð1 ð1 a1 x a1 x G(a) ¼ x e dx, a . 0 ¼ x e þ (a 1) xa2 ex dx 0
0
¼ (a 1)G(a 1)
0
(7:5:3)
If a is an integer n, then G(n) ¼ (n –1)G(n –1) and continued expansion yields G(n) ¼ (n– 1)! Hence the gamma function can be regarded as the generalization of the factorial function for all positive real numbers. The most useful fractional argument for the gamma function is 12 with pffiffiffiffi 1 G ¼ p (7:5:4) 2
100
Other Continuous Distributions
The incomplete gamma function Gx(a) is defined by ðx Gx (a) ¼ ja1 ej dj
(7:5:5)
0
We will use Eq. (7.5.5) later for the x2 distribution function FX(x) in Section 7.7. We shall now show that the density function given by Eq. (7.5.1) indeed integrates to 1. If we define the integral Ix by ðx l(lh)a1 elh dh (7:5:6) Ix ¼ G(a) 0 Then, substituting lh ¼ j in Eq. (7.5.6), we can rewrite ð 1 lx a1 j Ix ¼ j e dj G(a) 0
(7:5:7)
Applying Eq. (7.5.5) to Eq. (7.5.7), we have Ix ¼
Glx (a) G(a)
or
lim Ix ¼
x!1
G(a) ¼1 G(a)
thus showing the validity of Eq. (7.5.1) as a proper density function. If a is an integer, then we have the Erlang distribution. The gamma density family is shown in Fig. 7.5.1 with the parameter l ¼ 2 and a ¼ 0.5,1,1.5,2.5,3.5,4.5,5.5. The cumulative distribution function FX(x) is given by ð 1 x Gx (a) (7:5:8) FX (x) ¼ l(lj)a1 elj dj ¼ G(a) 0 G(a) This integral in Eq. (7.5.8) has no closed-form solution unless a is an integer, in which case it is the Erlang distribution given by Eq. (7.4.2). However, the integral given by Eq. (7.5.8) has been extensively tabulated. Reproductive Property of Gamma Distribution
If X1, X2, . . . , Xn are n independent gamma-distributed random variables with shape parameters a1, a2, . . . , an and having the same scale parameter l, then random variable
FIGURE 7.5.1
7.6
Weibull Distribution
101
Z ¼ X1 þ X2 þ þ Xn is also gamma-distributed with shape parameter b ¼ a1 þ a2 þ þ an. As a consequence of this property, since an exponential random variable is gamma-distributed with parameters (1,l), the sum of n exponential random variables is gamma-distributed with parameters (n,l), which is an Erlang distribution given by Eq. (7.4.1).
B 7.6
WEIBULL DISTRIBUTION
We will define the Weibull distribution as a three-parameter family of distribution functions, in which the parameter l . 0 is the scale parameter, a . 0 is the shape parameter, and m is the location parameter. It is defined by a a a l (x m)a1 e½l(xm) x . m (7:6:1) fX (x) ¼ 0 xm We will now find the cdf FX (x) of the Weibull distribution. We can write ðx a FX (x) ¼ a la (j m)a1 e½l(jm) dj
(7:6:2)
m
Substituting h ¼ ½l(j m)a and hence dh ¼ ½l(j m)a1 al dh, we can rewrite Eq. (7.6.2) as follows: ½l(xm)a ð ½l(xm) a eh dh ¼ eh , x.m (7:6:3) FX (x) ¼ 0
0
Substitution of the limits in Eq. (7.6.3) yields a 1 e½l(xm) x . m FX (x) ¼ 0 xm
and
FX (1) ¼ 1
(7:6:4)
Two families of Weibull distributions are shown for m ¼ 0 and a ¼ 2, l ¼ 0.5,1,2,3,4 (Fig. 7.6.1) and for l ¼ 2 and a ¼ 0.5,1,2,3,4,5 (Fig. 7.6.2). When m ¼ 0 and l ¼ 1 in Eqs. (7.6.1) and (7.6.4), then the resulting distribution is called the standard Weibull.
FIGURE 7.6.1
102
Other Continuous Distributions
FIGURE 7.6.2
The Weibull distribution was originally used for modeling fatigue data and is at present used extensively in engineering problems for modeling the distribution of the lifetime of an object that consists of several parts and that fails if any component part fails.
B 7.7
CHI-SQUARE DISTRIBUTION
The Laplace, Erlang, gamma, and Weibull distributions are all based on exponential distributions. The chi-square distribution straddles both the exponential and Gaussian distributions. The general gamma distribution has parameters (a, l). In the gamma distribution, if we substitute a ¼ n/2 and l ¼ 12, we obtain the standard chi-square distribution, defined by 8 (n=2)1 (x=2) x e > < , x.0 n=2 2 G(n=2) (7:7:1) fX (x) ¼ > : 0, x0 where n is the degrees of freedom for the x2 distribution. We can introduce another parameter s2 and rewrite Eq. (7.7.1) as 8 (n=2)1 (x=2s2 ) > e > <x , x.0 n=2 2 (7:7:2) fX (x) ¼ (2s ) G(n=2) > > : 0 , x0 If we substitute x ¼ x2 in Eq. (7.7.2), we obtain the familiar form of the chi-square distribution, given by 2
fX (x2 ) ¼
2
x2½(n=2)1 e(x =2s ) (2s2 )n=2 G(n=2)
(7:7:3)
where G(x) is the gamma function defined in Eq. (7.5.2). If n is even in Eq. (7.7.2), then G(n=2) ¼ (n=2 1)!, and if n is odd, then G(n=2) in Eqs. (7.7.1) pffiffiffiffi –(7.7.3) is obtained by iterating (n=2 1)G(n=2 1) with the final iterant Gð12Þ ¼ p. Equation (7.7.3) can also be obtained from the following. If X1, X2, P . . . , Xn are n i.i.d. Gaussian random variables with zero mean and variance s2, then X ¼ nk¼1 X k2 is x2 -distributed with n degrees of freedom.
7.7 Chi-Square Distribution
103
The cumulative chi-square distribution function FX(x) is given by integrating Eq. (7.7.2): 8 ð x n=21 (j=2s2 ) e < j dj x . 0 n=2 2 (7:7:4) FX (x) ¼ : 0 (2s ) G(n=2) 0 x0 This equation can be given in terms of the incomplete gamma function Gx(a) given by Eq. (7.5.5). Substituting h ¼ j/2s2 and dj ¼ 2s2 dh in Eq. (7.7.4), we obtain ðx FX (x) ¼
2
jn=21 e(j=2s ) 2s2 dj 2 n=21 G(n=2) 0 (2s )
ð x=2s2 ¼ 0
Gx=2s2 (n=2) hn=21 e(h) dh ¼ (7:7:5) G(n=2) G(n=2)
When x ! 1 then Gx=2s2 (n=2) ! G(n=2) and hence FX(x) ¼ 1. The cdf FX(x) in Eq. (7.7.5) has been extensively tabulated. The chi-square distribution is shown in Fig. (7.7.1) for the number of degrees of freedom n ¼ 2,3,4,5,6,10. The chi-square distribution is used directly or indirectly in many tests of hypotheses. The most common use of the chi-square distribution is to test the goodness of a hypothesized fit. The goodness-of-fit test is performed to determine whether an observed value of a statistic differs enough from a hypothesized value of a distribution to draw the inference whether the hypothesized quantity is the true distribution. Its utility is shown in Chapter 15. Example 7.7.1 The density function of fZ (z) of Z ¼ X 2 þ Y 2, where X and Y are independent random variables distributed as N(0, s2), can be found by substituting n ¼ 2 in Eq. (7.7.2), yielding 8 2 < e(z=2s ) z.0 FZ (z) ¼ 2 : 2s 0 z0
FIGURE 7.7.1
104
Other Continuous Distributions
which is a x2 density with 2 degrees of freedom. The corresponding cdf, FZ (z), is given by ( 2 1 e(z=2s ) z . 0 FZ (z) ¼ 0 z0
B 7.8
CHI AND OTHER ALLIED DISTRIBUTIONS
The Rayleigh and Maxwell distributions are special cases of the chi distribution. Just as the sum of the squares of n zero mean Gaussian random variables with variance s2 is chisquare-distributed with n degrees of freedom, we can consider that the sum of the square roots of the sum of the squares of n zero mean Gaussian random variables with variance s2 is chi-distributed with n degrees of freedom. Accordingly, let Zn be the random variable given by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (7:8:1) Zn ¼ X1 þ X2 þ þ Xn2 where fXi . i ¼ 1, . . . , ng are zero mean Gaussian random variables with variance s2. Then the x-density fZ (z) is given by 9 8 1 zn1 (1=2)(z=s)2 = < e z.0 fZ (z) ¼ 2n=21 G(n=2) sn (7:8:2) ; : 0 z0 where G(n/2) is the gamma function defined in Eq. (7.5.2). A family of chi distributions is shown in Fig. 7.8.1 for s ¼ 1.5 and n ¼ 1,2,3,5,7,9, 11,13,15. The distribution function FZ (z) is given by integrating Eq. (7.8.2): ðz 1 z n1 (1=2)(z=s)2 e dz z . 0 (7:8:3) Fz (z) ¼ n=21 G(n=2) sn 02 There is no closed-form solution for Eq. (7.8.3), but it can be expressed in terms of the incomplete gamma function GZ (z) defined in Eq. (7.5.5). By substituting y ¼ z2 =2s2 and
FIGURE 7.8.1
7.8 Chi and Other Allied Distributions
105
dz ¼ (s2 dy)=z in the in Eq. (7.8.3), we can write 2n=21 FZ (z) ¼ n=21 2 G(n=2) ¼
2n=21 2n=21 G(n=2)
ð z2 =2s2
y(n=21) ey dy
0
G(z2 =2s2 )
n G 2 2 (n=2) (z =2s ) ¼ 2 G(n=2)
(7:8:4)
where G(z2 =2s2 ) (n=2) is the incomplete gamma function defined in Eq. (7.5.5). Clearly, as z ! 1, G(z2 =2s2 ) (n=2) ! G(n=2), thus showing that FZ (1) ¼ 1. There are two very important special cases of the chi distribution when n ¼ 2 and n ¼ 3. Rayleigh Distribution (n 5 2)
When n ¼ 2 in the chi distribution of Eq. (7.8.2), we have ( z ) (1=2)(z=s)2 e z . 0 fZ (z) ¼ s2 0 z0
(7:8:5)
This is called the Rayleigh density and finds wide application in communications fading channels. If we have two independent zero mean Gaussian random variables X and Y with variance s2, and if we make the transformation X ¼ Z cos(Q) and Y ¼ Z sin(Q), pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 then Z ¼ X þ Y 2 is Rayleigh-distributed, which is a chi distribution with 2 degrees of freedom. Q is uniformly p distributed in the interval (0,2p]. The Rayleigh density is ffiffiffiffiffi shown in Fig. 7.8.2 for s ¼ 10,4,5. The cdf corresponding to the Rayleigh density can be expressed in a closed form as follows: ðz j j2 =2s2 FZ (z) ¼ e dj or, 2 0s ( 2 2 1 ez =2s z . 0 ¼ (7:8:6) 0 z0
FIGURE 7.8.2
106
Other Continuous Distributions
Maxwell Density (n 5 3)
This is one of the earliest distributions derived by Maxwell and Boltzmann from statistical mechanics considerations. They derived that the velocity vi of gas molecules in any direction i, based on the assumptions that the molecules are identical and distinguishable, and can occupy any energy state, follow the probability density function fV (vi ) ¼
e(1i =kT) Z
(7:8:7)
where 1i is the energy function given by 1i ¼ mv2i =2 with m as the mass of the particle, k is the Boltzmann constant, and T is the absolute temperature. Z is the normalization constant found from integrating Eq. (7.8.7) over all vi, or rffiffiffiffiffiffiffiffiffiffiffi ð 1 1 (mv2i =2kT) 2pkT e dvi ¼ 1 and Z ¼ Z 1 m so that the velocity density function along any direction follows a Gaussian distribution given by rffiffiffiffiffiffiffiffiffiffiffi m (mv2i =2kT) , i ¼ x,y,z (7:8:8) e fV (vi ) ¼ 2pkT From Eq. (7.8.8) the velocity vector V ¼ fvx,vy,vzg will be distributed with density function, " # m 3=2 m(v2x þ v2y þ v2z ) exp fV (vx ,vy ,vz ) ¼ (7:8:9) 2pkT 2kT Eq. (7.8.9) is a chi-square density with 3 degrees of freedom. The speed x of gas molecules will be defined by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x ¼ v2x þ v2y þ v2z Hence x is a distributed as a chi-density with three degrees of freedom. Its probability density can be derived as h m i3=2 2 f (x) ¼ 4p x2 emx =2kT , x . 0 (7:8:10) 2pkT If we substitute kT=m ¼ s2 in the Maxwell – Boltzmann distribution of Eq. (7.8.10), we obtain rffiffiffiffi 2 x2 x2 =2s2 e , x.0 (7:8:11) fX (x) ¼ p s3 Equation (7.8.11) is a special case of the chi density of Eq. (7.8.2) with n ¼ 3 degrees of freedom. The pdf values fX(x) for the Maxwell and Rayleigh densities are compared in pffiffiffiffiffi Fig. 7.8.3 for values of s ¼ 10, 4, and 5. The cdf FX(x) corresponding to the Maxwell density fX(x) is obtained by integrating Eq. (7.8.11) and is given by rffiffiffiffi ð x 2 2 j j2 =2s2 e dj (7:8:12) FX (x) ¼ p 0 s3
7.8 Chi and Other Allied Distributions
107
FIGURE 7.8.3
There is no closed form solution for Eq. (7.8.12). However, integrating Eq. (7.8.12) by parts, it can be expressed as rffiffiffiffi pffiffiffi ð x 2 2 x x2 =2s2 j2 =2s2 e FX (x) ¼ pffiffiffiffi e dj ps s p 0 rffiffiffiffi x 2 x x2 =2s2 e ¼ erf pffiffiffi x.0 (7:8:13) ps 2s where erf(x) is as defined in Eq. (6.4.6). Rice’s Density
In the Rayleigh distribution discussed earlier, the random variables X and Y must be zero mean with the same variances s2. In many communication problems involving fading channels, X and Y will not be zero mean p and will have mean values equal to mX and mY. If ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi the random variable Z is given by Z p ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 2 þffi Y 2 , then fZ (z) has a Rice density [49,50] with a noncentrality parameter m ¼ m2X þ m2Y , given by 2 z (z þ m2 ) mz fZ (z) ¼ 2 exp I0 2 (7:8:14) 2s2 s s where I0(z) is the zeroth-order modified Bessel function of the first kind. The general nth-order modified Bessel function is given by the series In (z) ¼
1 z n X
2
(z2 =4)k k! G(n þ k þ 1) k¼0
If n is an integer, then In(z) can also be given by ð 1 p z cos u In (z) ¼ e cos (nu) du p 0 and I0(z) the zeroth-order modified Bessel function by ð 1 X 1 p z cos u (z2 =4)k e du ¼ I0 (z) ¼ 2 p 0 k¼0 (k!) and I0(z), I1(z), I2(z), I3(z) are shown in Fig. 7.8.4.
(7:8:15)
(7:8:16)
(7:8:17)
108
Other Continuous Distributions
FIGURE 7.8.4
From Fig. 7.8.4 we see that I0(0) ¼ 1, and hence when m ¼ 0 (zero mean), then fZ (z) is Rayleigh-distributed. Rice’s density is shown in Fig. 7.8.5 for m ¼ 0,2,4,6,8,10,12,14, and m ¼ 0 corresponds to the Rayleigh density. The distribution function FZ(z) corresponding to Eq. (7.8.14) is given by integrating fZ (z): ðz j (j2 þ m2 ) mj exp I0 2 dj (7:8:18) FZ (z) ¼ 2 2 s 2s s 0 There is no closed-form solution for this integral.
Nakagami Density
The Nakagami distribution [49], like the Rayleigh density and unlike Rice’s distribution, also belongs to the class of central chi-square distributions. It is used for modeling data from multipath fading channels and has been shown to fit empirical results more generally than the Rayleigh distributions. Sometimes, the pdf of the amplitude of a mobile signal can also be described by the Nakagami m distribution.
FIGURE 7.8.5
7.8 Chi and Other Allied Distributions
109
The Nakagami distribution may be a reasonable means to characterize the back scattered echo from breast tissues to automate a scheme for separating benign and malignant breast masses. The shape parameter m has been shown to be useful in tissue characterization. Chi-square tests showed that this distribution is a better fit to the envelope than the Rayleigh distribution. Two parameters, m (effective number) and V (effective cross section), associated with the Nakagami distribution are used for the effective classification of breast masses. The Nakagami m density is defined by fX (x) ¼
2 h m im 2m1 mx2 =V x e G(m) V
(7:8:19)
where m is the shape parameter and V controls the spread of the distribution. The interesting feature of this density is that, unlike the Rice distribution, it is not dependent on the modified Bessel function. The Nakagami family is shown in Fig. 7.8.6 for V ¼ 1 and for m ¼ 3,2,1.5,1,0.75,0.5. The cdf FX(x) is given by ðx 2 h m im 2m1 mj2 =V FX (x) j e dj (7:8:20) 0 G(m) V This has no closed form solution. However, it can be given in terms of the incomplete gamma function by substituting y ¼ mj2/V and dj ¼ (V=2mj) dy in Eq. (7.8.20). This results in m 2 mj2 1 mj2 =V e dj FX (x) ¼ j V 0 G(m) m ð mx2 =V 2 2 mj2 1 emj =V ¼ dy G(m) V 2(mj2 =V) 0 ð mx2 =V 2 m y 1 y e dy ¼ G(m) 2y 0 ð mx2 =V Gmx2 =V (m) 1 m1 y y ¼ e dy ¼ G(m) G(m) 0 ðx
Since Gmx2 =V (m) ! G(m) as x ! 1 we have FX (1) ¼ 1:
FIGURE 7.8.6
(7:8:21)
110
Other Continuous Distributions
B 7.9
STUDENT-t DENSITY
The t distribution first developed by William Gosset under the pseudonym “Student” is used to estimate the confidence intervals for the unknown population mean where the variance is also unknown. In this case the statistic used for determining the confidence interval is given by T¼
m^ X mX pffiffiffi s= ^ n
(7:9:1)
and the distribution of T is governed by the Student-t distribution with density given by
nþ1 ½(nþ1)=2 G t2 2 1þ fT (t) ¼ pffiffiffiffiffiffi n n np G 2
1 , t , 1
(7:9:2)
where n is the number of degrees of freedom of T. It will be shown in Example 13.4.5 that if Z is a standard Gaussian random variable, Wn is a chi-square random variable with n degreespffiffiffiffiffiffiffiffiffiffiffi of freedom, and Z and Wn are independent, then the random variable T ¼ Z= Wn =n is t-distributed with n degrees of freedom. The Student-t distribution with n ¼ 2 is compared to a standard Gaussian in Fig. 7.9.1. After integrating the density function, we can write the distribution function FT (t) as 1 ½(nþ1)=2 ðt G n þ t2 2 1þ FT (t) ¼ dt n n 1 pffiffiffiffiffiffi np G 2 This is shown in Fig. 7.9.2.
FIGURE 7.9.1
(7:9:3)
7.10
Snedecor F Distribution
111
FIGURE 7.9.2 Limit of Student-t as n ! 1
We shall find the limit as n ! 1 in Eq. 7.9.2. We have ½(nþ1)=2 t2 2 lim 1 þ ¼ et =2 n!1 n 1 G nþ 1 2 ¼ pffiffiffiffiffiffi lim n!1 pffiffiffiffiffiffi n 2p np G 2
(7:9:4)
and hence ½vþ(1=2) G( nþ1 ) t2 1 2 lim pffiffiffiffiffiffi 2 n 1 þ ¼ pffiffiffiffiffiffi et =2 n!1 np G( ) n 2p 2
(7:9:5)
that is, as the number of degrees of freedom n goes to infinity, the Student-t tends to a standard Gaussian. If X1, X2, . . . , Xn are n independent identically distributed P Gaussian random variables with mean mX and standard deviation sX, then m^ X ¼ (1=n) ni¼1 Xi is also Gaussiandistributed with mean mX and variance s2X =n.PSince we do not know s2X , we use the sample variance given by s^ 2X ¼ 1=(n 1) ni¼1 (Xi m^ X )2 and s^ 2X is chi-squaredistributed with n – 1 degrees of freedom. Under these conditions the random variable T¼
(m^ X mX ) pffiffiffi s^ X = n
(7:9:6)
has a t distribution with n 2 1 degrees of freedom.
B 7.10
SNEDECOR F DISTRIBUTION
If X1 and X2 are two independent chi-squared-distributed random variables with n1 and n2 degrees of freedom, then the density of the random variable F ¼ (X1 =n1 )=(X2 =n2 ) is n þ n n n1 =2 1 2 1 G 2 n2 x(n1 2)=2 , x . 0 (7:10:1) fn1 ,n2 (x) ¼ n1 n2 1 þ n1 (n1 þn2 )=2 G x G 2 2 n2
112
Other Continuous Distributions
FIGURE 7.10.1
FIGURE 7.10.2
This density is shown in Fig. 7.10.1 for three cases: n1 ¼ 15, n2 ¼ 25: n1 ¼ 5, n2 ¼ 25: n1 ¼ 10, n2 ¼ 5. Note that in general fn1 ,n2 (x) is not equal to fn2 ,n1 (x). The corresponding distribution function is given by n þ n n n1 =2 1 2 1 ðx G 2 n2 (n1 2)=2 Fn2 , n1 (x) ¼ dj (7:10:2) n n 1 þ n (n1 þn2 )=2) j 0 1 2 1 G j G 2 2 n2 Equation (7.10.2) is shown in Fig. 7.10.2 for the same values of n1 and n2 as in Fig. 7.10.1. As n1 and n2 tend to infinity, then the density function becomes more sharply spiked around 1. The F distribution is used to draw inferences about the population variances in the case of two-sample situations.
B 7.11
LOGNORMAL DISTRIBUTION
A random variable X has lognormal density with parameters a, m, and s2 if the transformed random variable Y ¼ ln(X ) has a normal distribution with mean m and variance
7.11
Lognormal Distribution
113
FIGURE 7.11.1
s2. This density, defined only for positive values of x, is given by ( ) 1 1 ln (x a) m 2 exp fX (x) ¼ pffiffiffiffiffiffi , 2 s 2p s (x a)
x . a; m, s . 0
(7:11:1)
where a is the location parameter, m is the scale parameter, and s is the shape parameter. When a and m are zero, fX(x) is called the standard lognormal distribution with parameter s. The case where a ¼ 0 and m ¼ 1 is shown in Fig. 7.11.1 for s ¼ 0.4,0.5,1.0,1.5,2 for values of x . 0.1. The cumulative distribution function FX(x) is obtained by integrating Eq. (7.11.1) and is given by ðx
2 1 2 pffiffiffiffiffiffi e( ln (ja)m) =2s dj 2p s(j a) 0 ð ln (xa)m s 1 1 ln (x a) m 2 pffiffiffiffiffiffi eh =2 dh ¼ erf pffiffiffi ¼ 2 2p 2s 0
FX (x) ¼
(7:11:2)
where 1 1 x 2 pffiffiffi eh =2 dh ¼ erf pffiffiffi 2 2p 2 0
ðx
The cdf FX(x) is shown in Fig. 7.11.2 for a ¼ 0, m ¼ 1 and for the same values of s as the density function. In finance, prices for primary instruments such as stocks and bonds are often assumed to be lognormally distributed. A standard assumption for the evolution of a stock price is that in an interval Dt the fractional increase in the stock price, DS/S, is normally distributed with mean mDt and variance s2Dt. The lognormal distribution is also used to model the lifetime of units whose failures are of a fatigue– stress nature. Consequently, the lognormal distribution is an adjunct to the Weibull distribution when attempting to model these types of units.
114
Other Continuous Distributions
FIGURE 7.11.2
B 7.12
BETA DISTRIBUTION
The beta distribution is a two-parameter a and b family of density functions fX(x) for 0 x 1. It is often used to represent proportions and percentages. It has the following probability density function: fX (x) ¼
G(a þ b) a1 x (1 x)b1 , G(a)G(b)
0x1
(7:12:1)
The choices of the two parameters a and b makes it amenable to a variety of shapes. For example, for a ¼ b ¼ 1, fX(x) becomes the rectangular distribution. The triangular distribution arises when one parameter has the value 2 and the other is unity. If both parameters are less than unity and positive, the density becomes U-shaped, and if only one of the parameters is less than unity, it becomes J-shaped. The distribution is unimodal when both parameters are positive, and symmetric when they are equal. The pdf curves are shown in Fig. 7.12.1 for the cases described above. The beta distribution is related to the Student-t distribution [Eq. (7.9.2)] by the following transformation. If Z is a t-distributed
FIGURE 7.12.1
7.13
Cauchy Distribution
115
FIGURE 7.12.2
random variable with n degrees of freedom, then the transformation 1 Z X ¼ þ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 n þ Z2 is beta-distributed with a ¼ n/2 and b ¼ n/2. Example 7.12.1 We will give an example of how the beta distribution is used in stockmarket analysis. The analyst from his model of stock performances predicts before the start of each day that the proportion of the listed stocks has a beta distribution with parameters a ¼ 7.3 and b ¼ 5.2. The average value is a=(a þ b) ¼ 0:584. We want to find the probability that more than 70% of the stocks increase in value and this is given by ð 0:7 P{X . 0:7} ¼ 1 fX (x) dx ¼ 1 0:794 ¼ 0:206 0
This probability is 20.6%, as illustrated in Fig. 7.12.2.
B 7.13
CAUCHY DISTRIBUTION
Cauchy distribution resembles a normal distribution in appearance but has much heavier tails. However, in studying hypothesis tests that assume normality, seeing how the tests perform on data from a Cauchy distribution is a good indicator of how sensitive the tests are to heavy-tail departures from normality. The general Cauchy density is given by fX (x) ¼
b p½(x a)2 þ b2
(7:13:1)
where a is the location parameter and b is the shape parameter. It is an example of a pathological case in the sense the moments do not exist even though the first moment can be evaluated to be equal to 0 using the Cauchy principle value method. The Cauchy density is plotted for a ¼ 5 and b ¼ 2,3,4,5 in Fig. 7.13.1. Since the moments do not exist for the Cauchy distribution, it is characterized by the width at half the maximum value equal to 2b, called full width half maximum (FWHM). For b ¼ 2 the maximum is 0.159154 and the half-maximum is 0.079577. This is shown in Fig. 7.13.2. For comparison, a Gaussian distribution is superposed on the same figure for the same FWHM of 4 that is, a Gaussian with mean 5 and s ¼ 4/2.35482 ¼ 1.69864.
116
Other Continuous Distributions
FIGURE 7.13.1
FIGURE 7.13.2
FIGURE 7.13.3
7.14
Pareto Distribution
The distribution function FX(x) can be determined as follows: ðx 2 1 2 e( ln (ja)m) =2s dj FX (x) ¼ pffiffiffiffiffiffi 2p s(j a) 0 Substituting (j a)=b ¼ tan(u) in Eq. (7.13.2), we have ð tan1 {(xa)=b} sec2 (u)du FX (x) ¼ p sec2 (u) p=2 tan1 {(xa)=b} u 1 xa 1 ¼ ¼ tan1 þ p p=2 p b 2
117
(7:13:2)
(7:13:3)
The term FX(x) is shown in Fig. 7.13.3 for the same values of a and b as in Fig. 7.13.1.
B 7.14
PARETO DISTRIBUTION
Many synthetic and naturally occurring phenomena, including city sizes, incomes, word frequencies, and earthquake magnitudes, are distributed according to a power-law distribution. A power law implies that small occurrences are extremely common, whereas large instances are extremely rare. These can be modeled as a Pareto distribution. In addition, traffic measurements reveal features such as nonstationarity, long-range dependence, and significant fluctuations in different timescales (e.g., self-similarity). The essential features of the burstiness observed in measurements can be modeled as a heavy-tailed on-time and off-time distributions such as the Pareto distribution. The Pareto density is given by a b aþ1 fX (x) ¼ , x.0 (7:14:1) b xþb and the distribution function can be obtained from integrating Eq. (7.14.1): ðx a b aþ1 b a FX (x) ¼ dj ¼ 1 xþb 0b jþb
FIGURE 7.14.1
(7:14:2)
118
Other Continuous Distributions
The Pareto density and the distribution functions are plotted in Fig. 7.14.1 for a ¼ 5 and b ¼ 10,15,20. Note that the tails are long compared to other distributions. One of the uses of the Pareto distribution is to model the high end of the distribution of incomes.
B 7.15
GIBBS DISTRIBUTION
The Gibbs distribution has been widely used in image processing and pattern recognition problems. An example using this distribution in tomographic imaging is presented in Chapter 23. It is a generalization of the Maxwell – Boltzmann distribution and has origins in statistical mechanics. It is defined on a random field N as P(v) ¼
where,
1 ð1=TÞU(v) e Z
(7:15:1)
P U(v) ¼ wjk V(vj vk ) j,keN V(v)
Total energy function Potential function, where v is some vector field
wjk j, k
Weighting functions Pixels in neighboring field N
T
Absolute temperature that controls sharpness of distribution P Normalization constant given by Z ¼ v e(1=T)U(v) , also called the partition function
Z
The potential function V(v) depends on the local configuration of the field. Some of the properties of potential functions [38] are given below: 1. V(0) ¼ 0 2. V(v) 0: nonnegative 3. V(v) ¼ V(v): even function d 4. V(v) . 0 for v . 0 dv 5. lim V(v) ! 1 v!1
6.
sup 0v,1 2
7.
B 7.16
d dv V(v)
,1
d V(v) . 1 dv2
MIXED DISTRIBUTIONS
There are any number of derived and mixed distributions, depending on the application, and new distributions are also constantly being formulated. For example, the doubledouble-exponential or double-Laplace distribution, given by
p ðjxjÞ 1 p jxjb e a þ e f (x) ¼ (7:16:1) 2a 2b
7.17
Summary of Distributions of Continuous Random Variables
119
FIGURE 7.16.1
with f1 (x) ¼
p ðjxjÞ 1p e a : f2 (x) ¼ e 2a 2b
jxj b
is shown in Fig. 7.16.1 with p ¼ 0.3, a ¼ 1, b ¼ 4. This distribution has been specially constructed for modeling navigation errors over transatlantic flights consisting of both the pilot and instrument errors [51]. The pilot errors are modeled by f2(x) and the instrument errors, by f1(x). The instrument errors have a sharper peak than do pilot errors, which extend over a greater distance. Whereas the instrument are easier to eliminate, the pilot errors are more difficult. The composite error curve is f(x) shown in Fig. 7.16.1. The distributions given here are by no means exhaustive, and only some of the more important distributions are discussed. More comprehensive sets of distributions can be obtained from Web references W1 and W2 (see References at end of book). A complete list of all the densities and distributions discussed in this chapter is tabulated in Section 7.17.
B 7.17 SUMMARY OF DISTRIBUTIONS OF CONTINUOUS RANDOM VARIABLES Density Function
Name Uniform
Triangular
1 ba xa ; a,xb (b a)2 (x 2b þ a) (b a)2 b , x 2b a
Closed-Form Distribution Function xa ba 1 x a 2
a,xb 2 b a 1 x þ a 2b 2 1 2 ba b , x 2b a (continued)
120
Other Continuous Distributions
Density Function
Name Exponential Laplace
Closed-Form Distribution Function
1 lx e u(x), l . 0 l l ljxj e , l0 2
(1 elx )u(x), 1 lx e , 2
l.0
x0
1 12 elx , x . 0 Erlang
l(lx)n1 elx , (n 1)!
1
n integer
n integer; x . 0
l(lx)a1 elx , G(a)
a,l,x . 0
ala (x m)a1 e½l(xm) x.m 1 ðx2 =2Þ Standard Gaussian pffiffiffiffiffiffi e 2p
Weibull
Chi-square
k!
k¼0
x.0 Gamma
n1 X (lx)k elx
2
(x2 )(n=2)1 ex =2s n (2s2 )n=2 G 2
Gx (a) G(a) Incomplete gamma
a a
1 e½l(xm) , 1 1 x þ erf pffiffiffi 2 2 2 n Gx2 =2s2 n 2 , G 2
2
x.m
x ¼ x2
Incomplete gamma 1
Chi
2(n=2)1 G
n
xn1 (1=2)(x=s)2 e s2
2
x.0
n Gx2 =2s2 n2 G 2 Incomplete gamma
Rayleigh
z (z2 =s2 ) e , s2
Maxwell
rffiffiffiffi 2 x3 ðx2 =2s2 Þ e , p s3
Rice
x ½(x2 þm2 )=2s2 mx e I0 2 , s2 s
2
2
1 eðz =2s Þ , z . 0
z.0
x pffiffiffi s 2 erf 2s2
x.0
rffiffiffiffi 2 ðx2 =2s2 Þ e p
x.0 x.0
No closed form (continued)
7.17
Summary of Distributions of Continuous Random Variables
Density Function
Name Nakagami
Student-t(n)
Snedecor F(n1 ,n2 )
Closed-Form Distribution Function
2 m m 2m1 mx2 =V x e G(m) V
Gmx2 =V (m) G(m)
x.0
Incomplete gamma
vþ1 ½(vþ1)=2 G t2 2 1 þ pffiffiffiffiffiffi v v vp G 2 n þ n n n1 =2 1 2 1 x(n1 2)=2 2 n2 n n n1 (n1 þn2 )=2 1 2 G G 1þ x 2 2 n2 G
121
No closed form
No closed form
x.0 Lognormal
Beta
2 1 2 pffiffiffiffiffiffi e½ln (xa)m =2s 2p s(x a)
G(a þ b) a1 x (1 x)b1 G(a)G(b)
1 ln (x a) m pffiffiffi erf 2 s 2 No closed form
0x1 Cauchy
b p½(x a)2 þ b2
1 1 1 x a þ tan 2 p b
Pareto
aþ1 a b , x.0 b xþb
1
Definitions
Zero-order modified Bessel function ð 1 p x cos (u) I0 (x) ¼ e du p 0
b xþb
a ,
x.0
Error function ð 2 x j2 erf (x) ¼ pffiffiffiffi e dj p 0 Incomplete gamma function ðx Gx (a) ¼ ja1 ej dj 0
CHAPTER
8
Conditional Densities and Distributions
B 8.1 CONDITIONAL DISTRIBUTION AND DENSITY FOR P{A}= 0 In Section 2.2 we defined conditional probability for events A and B that are subsets of the sample space S as 9 P{A > B} = P{B j A} ¼ if P{A} = 0 (8:1:1) P{A} ; P{A > B} ¼ P{B j A}P{A} We will extend this to distributions FX(x) and densities fX(x). The conditional distribution FX(x j A), given that an event A has occurred, is defined as FX (x j A) ¼ P{X x j A} ¼
P{X x, A} P{A}
if
P{A} = 0
(8:1:2)
and if the event fX xg is independent of A, then PfX x j Ag ¼ PfX xg. The corresponding conditional density fX(x j A) is defined by dFX (x j A) dx P{x , X x þ Dx j A} ¼ lim Dx!0 Dx
fX (x j A) ¼
Properties of Conditional Distributions 1. FX (1 j A) ¼ 1; FX (1 j A) ¼ 0 2. FX (x j A) is right-continuous 3. FX (x2 j A) FX (x1 j A) ¼ P{x1 , X x2 j A} Ðx 4. FX (x j A) ¼ 1 fX (j j A) dj
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
122
(8:1:3)
8.1 Conditional Distribution and Density for P{A}= 0
123
Example 8.1.1 A die is tossed and, corresponding to the faces of the die, a random variable is defined by X ¼ 10k, k ¼ 1,2,3,4,5,6. The conditioning event A is given by A ¼ fthe toss resulted in an even numberg. We will first find the unconditional distribution FX(x) and then the conditional distribution FX(x j A). Using the same mapping techniques discussed in Chapter 2, the unconditional cdf is given by FX (x) ¼
6 1X u(x 10k) 6 k¼1
Since the sample space has been reduced to 3 points by the conditioning event A, the conditional cdf FX(x j A) is given by Fx (x j A) ¼ ¼
P{X 10k, X even} FX (10k), k ¼ 2,4,6 ¼ P{X even} 1=2 3 1X u(x 20k) 3 k¼1
Both these distributions are shown in Fig. 8.1.1. Conditioning Event A Depends on x. We shall now consider the case when the event A is dependent on x. Three cases arise: 1. A ¼ {X x1 }: Eq. (8.1.2) takes the form: FX (x j A) ¼
P{X x, X x1 } P{X x1 }
(8:1:4)
Two situations arise: (a) x . x1 : Since x . x1, the joint event fX x, X x1g ¼ fX x1g, and hence Eq. (8.1.4) can be rewritten as FX (x j A) ¼
P{X x1 } ¼1 P{X x1 }
(8:1:5)
(b) x x1: In this case the joint event fX x, X x1g ¼ fX xg, and hence Eq. (8.1.4) becomes FX (x j A) ¼
P{X x} FX (x) ¼ P{X x1 } FX (x1 )
FIGURE 8.1.1
(8:1:6)
124
Conditional Densities and Distributions
The corresponding density function is given by 8 < fX (x) fX (x j A) ¼ FX (x1 ) : 0
x x1
(8:1:7)
x . x1
2. A ¼ fx1 , X x2g with x1 , x2 Analogous to Eq. (8.1.4), we have the following: FX (x j A) ¼
P{X x, x1 , X x2 } P{x1 , X x2 }
(8:1:8)
Here three situations arise: (a) x . x2: FX (x j A) ¼
P{x1 , X x2 } ¼1 P{x1 , X x2 }
(8:1:9)
(b) x1 . x x2: FX (x j A) ¼
P{x1 , X x} FX (x) FX (x1 ) ¼ P{x1 , X x2 } FX (x2 ) FX (x1 )
(8:1:10)
FX (x j A) ¼
P{1} ¼0 P{x1 , X x2 }
(8:1:11)
(c) x x1:
The corresponding density function is given by 8 0 x x1 > > < fX (x) x1 , x x2 fX (x j A) ¼ > F (x ) FX (x1 ) > : X 2 0 x . x1
Example 8.1.2
(8:1:9)
A random variable has a Gaussian distribution, given by ( " #) 1 1 x4 2 fX (x) ¼ pffiffiffiffiffiffi exp 2 2 2 2p
We will find the conditional distribution FX(x j A) and the conditional density fX(x j A) given that (1) A ¼ fX 4.5g and (2) A ¼ f1.5 , X 4.5g. Using Eq. (8.1.6), we obtain 1. A ¼ fX 4.5g: FX (x j A) ¼
FX (x) 1 1 x4 ¼ 1 þ erf pffiffiffi 9 9 2 2 2 FX FX 2 2
and using Eq. (8.1.10), we obtain 2. A ¼ f 1.5 , X 4.5 g:
1 x 4) 3 pffiffiffi 1 þ erf FX FX (x) FX (x1 ) 2 2 (2 2 FX (x j A) ¼ ¼ 9 3 FX (x2 ) FX (x1 ) FX FX 2 2
8.1 Conditional Distribution and Density for P{A}= 0
125
FIGURE 8.1.2
where 2 erf(x) ¼ pffiffiffiffi p
ðx
2
ej dj
0
The corresponding density functions are as follows for (1) ( " 2 #) 1 1 x4 pffiffiffiffiffiffi exp 2 2 2 2p fX (x j A) ¼ 9 FX 2 and for (2)
( " #) 1 1 x4 2 pffiffiffiffiffiffi exp 2 2 2 2p fX (x j A) ¼ 9 3 FX FX 2 2
The conditional distribution and densities FX(x j A) and fX(x j A) along with the unconditional distributions and densities FX(x) and fX(x) are shown in Fig. 8.1.2 for (1) A ¼ fX 4.5g and in Fig. 8.1.3 for (2) A ¼ f1.5 , X 4.5g.
FIGURE 8.1.3
126
Conditional Densities and Distributions
B 8.2 CONDITIONAL DISTRIBUTION AND DENSITY FOR P{A} 5 0 In Section 8.1 we assumed that the probability of the conditioning event A is not zero. We will now determine the conditional probabilities when PfAg ¼ 0. We need to find PfB j X ¼ x1g. Since PfX ¼ x1g ¼ 0 for a continuous random variable, we have to use limiting forms to determine this probability. We will write PfB j X ¼ x1g as P{B j X ¼ x1 } ¼ lim P{B j x1 , X x1 þ Dx} Dx!0
(8:2:1)
The conditional probability PfB j x1 , x x1 þ Dxg in Eq. (8.2.1) can be written as follows: P{B j x1 , X x1 þ Dx} ¼
P{B, x1 , X x1 þ Dx} P{x1 , X x1 þ Dx}
¼
P{x1 , X x1 þ Dx j B}P{B} FX (x1 þ Dx) FX (x1 )
¼
FX {x1 þ Dx j B} FX {x1 j B}P{B} FX (x1 þ Dx) FX (x1 )
(8:2:2)
Dividing numerator and denominator of Eq. (8.2.2) by Dx and taking the limit as Dx ! 1 results in P{B j X ¼ x1 } ¼ lim
Dx!0
¼
{FX {x1 þ Dx j B} FX {x1 j B}P{B}g=Dx {FX (x1 þ Dx) FX (x1 )}=Dx
fX (x1 j B)P{B} fX (x1 )
(8:2:3)
Case of B: fY 5 yg
In an analogous manner we can find an expression for the conditional probability PfB j X ¼ xg when B is the event B:fY ¼ y). In this case we can write P{Y ¼ y j X ¼ x} ¼ lim P{y , Y y þ Dy j x , X x þ Dx} Dx!0 Dy!0
¼ lim
Dx!0 Dy!0
P{y , Y y þ Dy, x , X x þ Dx} P{x , X x þ Dx}
Dividing the numerator and denominator of Eq. (8.2.4) by Dx Dy, we have, lim P{y , Y y þ Dy j x , X x þ Dx}
Dx!0 Dy!0
½P{y , Y y þ Dy j x , X x þ Dx}=DxDy Dx!0 ½P{x , X x þ Dx=DxDy
¼ lim
Dy!0
¼
fXY (x,y) Dy fX (x)
(8:2:4)
8.2 Conditional Distribution and Density for P{A} 5 0
127
Hence
lim
Dx!0 Dy!0
P{y , Y y þ Dy j x , X x þ Dx} ¼ fYjX ( y j X ¼ x) Dy ¼ fYjx ( y j x) ¼
fXY (x,y) fX (x)
(8:2:5)
The conditional distribution FYjX( y j X xg ¼ PfY y j X xg can be obtained from the basic definition of conditional probability as follows:
FYjX ( y j X x) ¼ P{Y y j X x} ¼
P{Y y, X xg FXY (x, y) ¼ P{X x} FX (x)
(8:2:6)
Even though Eq. (8.2.5) is of exactly the same form as Eq. (8.2.6), it cannot be obtained from the definition of conditional probability but must be obtained from limiting considerations.
Point of Clarity
Since fYjX( y j x) has been defined as fYjX( y j X ¼ x), it is not the derivative with respect to y of the distribution FYjX ( y j x), which has been defined as FYjX( y j X x). Bearing this in mind, we can write the following equations for clarity: dFYjX ( y j X x) dFyjX ( y j x) ¼ ¼ fYjX ( y j X x) dy dy (8:2:7) dFYjX ( y j X ¼ x) ¼ fYjX ( y j X ¼ x) ¼ fYjX ( y j x) dy Example 8.2.1 The probability that a machine fails in x months is given by (1 2 e 2x/d) u(x). If the machine failed sometime before c months, it is desired to find the conditional distribution FX(x j X c) and the corresponding conditional density. Here we can use the definition of conditional distribution and write for FX(x j X c): FX (x j X c) ¼ P{X x j X c} ¼
¼
P{X x, X c} P{X c}
8 1 ex=d > > > < 1 ec=d
0,xc
> > > :
x.c x0
1 0
The corresponding density function for the event fX cg is dex=d fX (x j X c) ¼ 1 ec=d 0
0,xc otherwise
128
Conditional Densities and Distributions
FIGURE 8.2.1
We can now find the conditional distribution FX(x j X ¼ c) if it failed at x ¼ c months. Since PfX ¼ cg ¼ 0, this has to be solved using limiting considerations: FX (x j X ¼ c) ¼ lim P{X x j c , X c þ Dc} Dc!0
¼ lim
Dc!0
P{X x, c , X c þ Dc} P{c , X c þ Dc}
P{c , X c þ Dc j X x}P{X x}=Dc Dc!0 P{c , X c þ D c}=Dc
¼ lim
¼ fX (c j X x)
FX (x) fX (c, X x) FX (x) ¼ fX (c) FX (x) fX (c)
Hence FX (x j X ¼ c) ¼
1 x.c 0 xc
and the corresponding density function for the event fX ¼ cg is given by fX (x j X ¼ c) ¼ d(x c) These distribution and density functions are shown in Fig. 8.2.1 for c ¼ 2, d ¼ 2. Example 8.2.2
The joint density function of random variables X and Y is (1 fXY (x, y) ¼
p 0
x2 þ y2 1 otherwise
The marginal density function fX(x) is obtained by integrating fXY(x,y) over all y: ffi ð pffiffiffiffiffiffiffi 1x2
pffiffiffiffiffiffiffiffiffiffiffiffiffi dy 2 1 x2 fX (x) ¼ pffiffiffiffiffiffiffiffi ¼ p 1x2 p
8.2 Conditional Distribution and Density for P{A} 5 0
129
FIGURE 8.2.2
From Eq. (8.2.5), the conditional density fYjX (y j X ¼ x) is given by 8 1 < pffiffiffiffiffiffiffiffiffiffiffiffi fXY (x, y) 1=p ffi x2 þ y2 1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 2 1 x2 fYjX ( y j X ¼ x) ¼ fX (x) 2 1 x2 =p : 0 otherwise Note that for each value of x the pdf fYjX (y j X ¼ x) is a constant and not a function of y. The corresponding conditional pffiffiffiffiffiffiffiffiffiffiffiffiffi distribution function FYjX (y j X ¼ x) is obtained by integrating the pdf from 1 x2 to y as shown below: ðy 1 y 1 FYjX (y j X ¼ x) ¼ pffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi dh ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi þ 2 2 2 2 2 1x 1x 2 1 x In Fig. 8.2.2, the conditional pdf fYjX (y j X ¼ x) is shown as a function of x and the conditional cdf FYjX (y j X ¼ x) is shown as a function of y for x = 0.5. Example 8.2.3 This is an interesting example that again illustrates finding the conditional probability when the probability of the conditioning event is zero. Two friends A and B plan to meet between 7:00 and 8:00 p.m. However, they agree that each of them will wait only 15 min for the other. Each of them arrives independently at a random time between 7 and 8 p.m. We have to compute the probability that they will meet given that A arrives at 7:20 p.m. Let us first define these random variables. X ¼ {time of arrival of A after 7:00 p.m. in minutes} Y ¼ {time of arrival of B after 7:00 p.m. in minutes} The pdf fX(x) and fY( y) are both uniformly distributed in (0, 60] as shown in Fig. 8.2.3. Since X and Y are independent, the joint density fXY(x,y) ¼ fX(x) . fY( y) and is given by 1 1 1 fXY(x,y) ¼ 60 60 ¼ 3600 : The 60 60 Cartesian product space is shown in Fig. 8.2.4.
FIGURE 8.2.3
130
Conditional Densities and Distributions
FIGURE 8.2.4
If A comes at time x minutes after 7:00 p.m., then for the meeting to take place, we have to find the probability of the event j Y 2 X j 15 under the condition X ¼ x. Hence, we can write PfY 2 X j 15 j X ¼ xg ¼ Pf215 þ X Y 15 þ X j X ¼ xg, and we have to find the area under the shaded portion in Fig. 8.2.4. In essence, we have to integrate fY( y) in three different regions: (1) 0 , y x þ 15, 0 , x ¼ 15, (2) x 2 15 , y x þ 15, 15 , x 45, and (3) x þ 15 , y 60, 45 , x 60. Performing the indicated integration, we obtain 8 ð 15þx > dx > > 0 , x , 15 > > 60 > 0 > > < ð 15þx dx P{A and B meeting j X ¼ x} ¼ 15 , x 45 > 60 > 15þx > > ð 60 > > dx > > 45 , x 60 : 60 15þx 8 15 þ x > > 0 , x 15 > > 60 > > < 1 ¼ 15 , x 45 > 2 > > > > > : 75 x 45 , x 60 60 The probability that A and B meet if A arrives exactly x minutes after 7:00 p.m. is shown in Fig. 8.2.5. From the curve, the Pfmeet j X ¼ 20g ¼ 0.5. The total probability of A and B meeting is given by ð 60 P{meet} ¼
P{meet j x ¼ x}fX (x) 0
¼
1 60 30 15 105 7 þ þ ¼ ¼ 0:4375 ¼ 60 4 4 4 4:60 16
8.3 Total Probability and Bayes’ Theorem for Densities
131
FIGURE 8.2.5
B 8.3 TOTAL PROBABILITY AND BAYES’ THEOREM FOR DENSITIES We will use Eq. (8.2.3), shown in Eq. (8.3.1) as the starting point to derive the total probability law for density functions: P{B j X ¼ x1 } ¼
fX (x1 j B)P{B} fX (x1 )
(8:3:1)
Cross-multiplying and integrating both sides of the resulting equation, we obtain ð1
ð1 P{B j X ¼ x1 }fX (x1 )dx1 ¼
1
fX (x1 j B) P{B}dx1
(8:3:2)
1
In Eq. (8.3.2) we obtain the term ð1
ð1 fX (x1 j B)P{B}dx1 ¼ P{B}
1
fX (x1 j B)dx1 ¼ P{B} 1
and hence Eq. (8.3.2) becomes ð1 P{B j X ¼ x}fX (x)dx ¼ P{B}
(8:3:3)
1
where the subscript on x has been omitted without loss of generality. Equation (8.3.3) is the total probability law for density functions. We can now formulate Bayes’ theorem for density functions. From Eq. (8.3.1) we can write fX (x j B) ¼
P{B j X ¼ x}fX (x) P{B} P{B j X ¼ x}fX (x) 1 P{B j X ¼ x}fX (x)dx
¼ Ð1
(8:3:4)
132
Conditional Densities and Distributions
where fX(x j B) is the a posteriori pdf after observing the event B given the a priori pdf fX(x). If B is the event B ¼ {Y ¼ y}, then Eq. (8.3.4) can be written as fYjX (y j x)fX (x) 1 fYjX (y j x)fX (x)dx
fXjY (x j y) ¼ Ð 1
(8:3:5)
Equation (8.3.5) is Bayes’ theorem for connecting the a priori density fX(x) with the a posteriori density fXj Y (x j y) after observing the event fY ¼ yg.
Example 8.3.1 Let X be the random variable that a navigation system fails before x hours. Three suppliers A, B, and C have supplied the system to airlines with probabilities PfAg ¼ 0.35, PfBg ¼ 0.25, PfCg ¼ 0.4. The conditional probabilities of failure for the suppliers A, B, C are as follows: FX (x j A) ¼ (1 eax )u(x), a ¼ 1:625 104 FX (x j B) ¼ (1 ebx )u(x), b ¼ 5:108 104 FX (x j C) ¼ (1 ecx )u(x),
c ¼ 2:877 104
1. From the conditional probabilities of failure, we can find the probability density fX(x) as follows: fX (x j A) ¼ aeax u(x),
a ¼ 1:625 104 : P{A} ¼ 0:35
fX (x j B) ¼ bebx u(x),
b ¼ 5:108 104 : P{B} ¼ 0:25
fX (x j C) ¼ cebx u(x), c ¼ 2:877 104 : P{C} ¼ 0:40 and fX (x) ¼ fX (x j A)P{A} þ fX (x j B)P{B} þ fX (x j C)P{C} ¼ (0:56875eax þ 1:277ebx þ 1:1508ecx )104 u(x) 2. If the navigation system fails after x hours, we will find the probabilities PfA j X . xg, PfB j X . xg, PfC j X . xg. P{X . x} is given by P{X . x} ¼ (1 FX (x j A)ÞP{A} þ (1 FX (x j B))P{B} þ (1 FX (x j C))P{C} ¼ eax 0:35 þ ebx 0:25 þ ecx 0:40 The probability PfA j X . xg can be found from the equation
P{A j X . x} ¼
P{X . x j A}P{A} (1 FX (x j A))P{A} ¼ P{X . x} P{X . x}
8.3 Total Probability and Bayes’ Theorem for Densities
133
with similar equations for PfB j X . xg and PfC j X . xg. Hence these probabilities can be given by P{A j X . x} ¼
P{B j X . x} ¼
P{C j X . x} ¼
eax 0:35
eax 0:35 þ ebx 0:25 þ ecx 0:40
eax 0:35
ebx 0:25 þ ebx 0:25 þ ecx 0:40
ecx 0:40 eax 0:35 þ ebx 0:25 þ ecx 0:40
3. If the navigation system fails exactly at time X ¼ x, we will find PfA j X ¼ xg, P{B j X ¼ x}; and P{C j X ¼ x}. From Eq. (8.2.3) we have P{A j X ¼ x} ¼
fX (x j A)P{A} 0:56875 104 eax ¼ fX (x) fX (x)
P{B j X ¼ x} ¼
fX (x j A)P{B} 1:277 104 ebx ¼ fX (x) fX (x)
P{C j X ¼ x} ¼
fX (x j A)P{C} 1:1508 104 ecx ¼ fX (x) fX (x)
4. We will find the most likely supplier in part (3) if x ranges from 1000 to 10,000 and the total probability of a wrong decision Pe. The most likely supplier if the system failed at exactly x hours can be obtained by drawing the graphs of the probabilities PfA j X ¼ xg, PfB j X ¼ xg, and PfC j X ¼ xg as shown in Fig. 8.3.1. From the graph in Fig. 8.3.1 we can see that if the system fails between x ¼ 1000 and x ¼ 5629.17 h, the most likely supplier is C, and if it fails between x ¼ 5629.17 and
FIGURE 8.3.1
134
Conditional Densities and Distributions
10,000 h, the most likely supplier is A. The conditional probability PfB j X ¼ xg is smaller than the other two conditional probabilities in the two ranges. The probability of wrong decision Pe is given by Pe ¼ P{X 5629:17 j A}P{A} þ P{X . 5629:17 j C}P{C} þ P{B} Since supplier B does not figure in the wrong decision, PfBg appears without any multiplier. Hence P{wrong decision} ¼ Pe ¼ ½1 e5629:17a 0:35 þ ½1 (1 e5629:17c ) 0:4 þ 0:25 ¼ 0:539
CHAPTER
9
Joint Densities and Distributions
B 9.1
JOINT DISCRETE DISTRIBUTION FUNCTIONS
If the random variables fX ¼ X1, X2, . . . , Xng, are discrete, then we can define a joint probability mass function as pX1 Xn (x1 , . . . , xn ) ¼ P{X1 ¼ x1 , . . . , Xn ¼ xn }
(9:1:1)
and the total sum n X m
n X n X j
pX1 Xn (xi , . . . , xm ) ¼ 1
i
In addition, the marginal distributions are defined by n X
pX1 Xn (x1 , . . . , xk ) ¼ pX1 Xn1 (x1 , . . . , xn1 )
(9:1:2)
k
Example 9.1.1 (Discrete) An urn contains 4 red, 5 green, and 6 blue balls, and 3 balls are taken at random. Given the number of red balls represented by the random variable X and the number of green balls by the random variable Y, we want to find the joint probability pXY (k,m) of PfX ¼ k, Y ¼ mg, where k ranges from 0 to 4 and m ranges from 0 to 5. The total number of points in the sample space is n! 15! n C(n,r) ¼ ¼ ¼ ¼ 455 r r!(n r)! 3!12! The number of ways in which 3 balls can be picked from 15 balls is to choose k balls from 4 red balls, m balls from 5 green balls, and 3 2 k – m balls from 6 blue balls. Hence, the probability pXY (k,m) is given by C(6, 3 k m)C(4, k)C(5, m) k þ m 3 pXY (k,m) ¼ 0 otherwise
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
135
136
Joint Densities and Distributions
FIGURE 9.1.1
These probabilities are shown in Fig. 9.1.1 2 p(0,0) 1 6 p(1,0) 6 PXY ¼ C(15,3) 4 p(2,0) p(3,0)
for all values of k and m. 3 p(0,1) p(0,2) p(0,3) p(1,1) p(1,2) p(1,3) 7 7 p(2,1) p(2,2) p(2,3) 5 p(3,1) p(3,2) p(3,3)
As the probability mass functions PfX ¼ k) and PfY ¼ m) appear as sums in the margin, they are referred to as marginal probability mass functions. Thus, the probability of 2 red balls PfX ¼ 2g ¼ 66/455 and the probability of 3 green balls PfY ¼ 3g ¼ 10/455.
B 9.2
JOINT CONTINUOUS DISTRIBUTION FUNCTIONS
The joint distribution of n random variables fX1, X2, . . . , Xng, is the probability of the joint event fX1 x1g, . . . , Xn xn and is given by FX1 Xn (x1 , . . . , xn ) ¼ P{X1 x1 , . . . , Xn xn }
(9:2:1)
In particular, if X and Y are two random variables, the joint distribution function FXY (x,y) is given by FXY (x,y) ¼ P{X x, Y y}
(9:2:2)
The corresponding density function is given by fXY (x,y) ¼
@2 FXY (x,y) @x@y
(9:2:3)
Using the definition of the second derivative, Eq. (9.2.3) can also be given in terms of the distribution function as follows: FXY (x þ D x, y þ Dy) þ FXY (x,y) FXY (x þ D x,y) þ FXY (x,y þ Dy) lim fXY (x,y) ¼ D x!0 Dy!0 D xDy
(9:2:4)
Similarly, the distribution function FXY (x,y) can also be given in terms of the density function by ðx ðy FXY (x,y) ¼ fXY (j,h)dhdj (9:2:5) 1 1
9.2
Joint Continuous Distribution Functions
137
The marginal density functions fX(x) and fY( y) are obtained by integrating the joint density function FXY (x,y) over all y and x, respectively, giving ð1 fX (x) ¼
ð1 fXY (x,y)dy;
fY ( y) ¼
1
fXY (x,y)dx
(9:2:6)
1
Properties of Joint Densities and Distributions 1. FXY (1, 1) ¼ 0; FXY (1,1) ¼ 1 2. FXY (1,y) ¼ 0; FXY (x,1) ¼ 0 3. FXY (x,1) ¼ FX (x); FXY (1,y) ¼ FY (y) 4. P{x1 , X x2 , Y y} ¼ FXY (x2 ,y) FXY (x1 ,y) P{X x, y1 , Y y2 } ¼ FXY (x,y2 ) FXY (x,y1 ) 5. P{x1 , X x2 , y1 , Y y2 } ¼ FXY (x2 ,y2 ) FXY (x2 ,y1 ) FXY (x1 ,y2 ) þ FXY (x1 ,y1 ) 6. @FXY (x,y) ¼ @x @FXY (x,y) ¼ @y
(9:2:7)
(9:2:8)
ðy fXY (x,h)dh 1 ðx
(9:2:9) fXY (j,y)dj
1
In many problems we are given fXY (x,y) in the region {(x1,x2], ( y1,y2]} and the distribution function FXY (x,y) is desired in the entire XY plane. In these cases, we can visualize the XY plane and delineate the various regions as shown in Fig. 9.2.1 for which FXY (x,y) can be determined. Given FXY (x,y) in region I, we find that using the properties enunciated earlier: FXY (x,y) ¼ 0 in region V, FXY (x,y) ¼ FX (x) in region II, FXY (x,y) ¼ FY ( y) in region III, and FXY (x,y) ¼ 1 in region IV. We shall give three different examples to illustrate how the joint distribution function can be determined.
FIGURE 9.2.1
138
Joint Densities and Distributions
Example 9.2.1 (Regions Bounded by Constants) The joint pdf fXY (x,y) is given by K(8 x y) 1 , x 3, 1 , y 2 fXY (x,y) ¼ 0 otherwise The constant K can be found by integrating over the range, yielding ð3 ð2 1 K(8 x y)dy dx ¼ 9K ¼ 1 or K ¼ 9 1 1 We plot the regions in Fig. 9.2.2 as shown in Fig. 9.2.1.
Region V: x 1 or y 1 FXY (x,y) ¼ 0 Region IV: 2x . 3, y . 2 FXY (x,y) ¼ 1 Region I: 1 , x 3, 1 , y 2 y ðx 1 1 h2 (8 j h)dh dj ¼ 8h jh 2 1 1 19 19 ðx 1 y2 15 8y jy þ j ¼ dj 2 2 19 x 1 j2 y jy2 j2 15j 8yj þ ¼ 9 2 2 1 2 2 1 j2 y jy2 j2 15j 8yj þ ¼ 9 2 2 2 2 2 2 2 2 1 x y xy x y 15x 15y 8xy þ7 þ þ ¼ 9 2 2 2 2 2 2 ðx ðy
FIGURE 9.2.2
9.2
Joint Continuous Distribution Functions
139
Region II: 1 , x 3, y . 2 [marginal distribution FX(x)] 1 x2 13x 6 FXY (x,2) ¼ FX (x) ¼ þ 9 2 2 Region III: x . 3, 1 , y 2 [marginal distribution FY ( y)] FXY (3,y) ¼ FY ( y) ¼
1 2 y þ 12y 11 9
By differentiating the distribution function, we can write the density function for the various regions as follows: 8 0 > > > >1 > > > > 9 (8 x y) > > <1 (12 2y) fXY (x,y) ¼ 9 > > > > 1 13 > > x > > > 9 2 > : 0
x1
or y 1
1 , x 3, 1 y 2 x . 3, 1 y 2 1 , x 3, y . 2 x . 3, y . 2
We can also conclude that since fXY (x,y) = fX (x) fY ( y), the random variables X and Y are not independent. Having determined the joint probability FXY (x,y), we can find the probability for other regions such as f1.5 , X 2.5, 1 , Y 1.5g as shown in Fig. 9.2.3. ð 2:5 ð 1:5 P{1:5 , X 2:5, 1 , Y 1:5} ¼ 1:5
1
1 (8 x y)dy dx 9
¼ FXY (2:5, 1:5) FXY (1:5, 1:5) FXY (2:5, 1) þ FXY (1:5, 1) ¼ 0:4167 0:1528 0 0 ¼ 0:2639
FIGURE 9.2.3
140
Joint Densities and Distributions
We can also find the conditional probability of Y y given that X x as follows FXY (x,y) FX (x) 1 x2 y xy2 x2 y2 15x 15y 8xy þ7 þ þ 9 2 2 2 2 2 2 2 ¼ 1 x 13x 6 þ 9 2 2
FY j X (y j X x) ¼
¼
y2 þ xy x 15y þ 14 x 12
At the endpoints x ¼ 3 and y ¼ 2, we have FY j X ( y j X 3) ¼ FY ( y) and FY j X (2 j X 3) ¼ FY ( y) ¼ 1. The corresponding conditional density function fY j X( y j X x) is given by fY j X (y j X x) ¼
x þ 2y 15 , x 12
1,x3 1,y2
We will now find the conditional density fY j X ( y j X ¼ x) from Eq. (8.2.5) 1 fXY (x,y) 9 (8 x y) 2(8 x y) ¼ ¼ fY j X (y j X ¼ x) ¼ 1 13 fX (x) 13 2x x 9 2 in the region 1 , x 3, 1 , y 2. The corresponding distribution function FY j X ( y j X ¼ x) in the region 1 , x 3, 1 , y 2 is obtained by integrating the density function and is given by ðy 2(8 x h) dh FY j X (y j X ¼ x) ¼ 13 2x 1 ¼
y2 þ 2xy 2x 16y þ 15 2x 13
We see clearly from this example that FY j X ( y j X x) is different from FY j X ( y j X ¼ x) and the corresponding conditional density functions fY j X ( y j X x) and fY j X ( y j X ¼x) are also different. Note that fY j X ( y j X ¼ x) has been defined as fY j X ( y j x) in Chapter 8. Example 9.2.2 (One Region Bounded by a Function) In this example one of the bounds on the region is a functional inequality. The joint pdf fXY (x,y) of two random variables X and Y is given by (8 (1 þ 3xy) 0 , x y 1 fXY (x,y) ¼ 7 0 otherwise We have to find the joint distribution function FXY (x,y) in the entire xy plane. Integrating first along the h axis and then along the j axis (Fig. 9.2.4), we have ðx ðy 8 FXY (x,y) ¼ (1 þ 3jh)dh dj 0 j7 ¼
1 3x4 þ 6x2 y2 4x2 þ 8xy , 7
0,xy1
9.2
Joint Continuous Distribution Functions
141
FIGURE 9.2.4
Integrating along the j axis first and then along the h axis (Fig. 9.2.4), we have ðx ðh 8 (1 þ 3jh)dj dh FXY (x,y) ¼ 0 0 7 I |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl} ðy ðx 8 (1 þ 3jh)dj dh þ x 07 II |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} 1 1 ¼ ð3x4 þ 4x2 Þ þ ð6x4 þ 6x2 y2 8x2 þ 8xyÞ 7 7 1 ¼ ð3x4 þ 6x2 y2 4x2 þ 8xyÞ; 0 , x y 1 7 Example 9.2.3 (Both Regions Bounded by Functions) The joint probability density function of two random variables X and Y is given by 8 2 > 0,x1 > > < 6 k(1 þ 2xy) 4 1 fXY (x,y) ¼ 0 , x2 , y x þ 1 > > 2 > : 0 otherwise The two boundaries are shown in Fig. 9.2.5.
FIGURE 9.2.5
142
Joint Densities and Distributions
The constant k is found by integrating fXY (x,y) over the ranges of definition as shown below: ð 1=2 ð xþ1=2 ð1 ð1 k(1 þ 2xy)dy dx þ k(1 þ 2xy)dy dx ¼ 1 0
x2
1=2 x2
Solving the above equation we obtain, k
161 ¼1 192
or k ¼
192 161
Method 1: Marginal Distributions by Integrating fXY (x,y). We will now find the marginal density fX (x) by integrating fXY (x,y) over all y. In the region 0 , x 12, we have ð xþ1=2 192 (1 þ 2xy)dy fX (x) ¼ 161 x2 48 1 ¼ 2 þ 5x þ 4x3 4x 5 , 0 , x 161 2 In the region 12 , x 1, we have ð1
192 (1 þ 2xy)dy x2 161 192 ¼ 1 þ x x2 x5 , 161
fX (x) ¼
1 ,x1 2
By integrating fX (x) in these two regions, we can write the distribution function FX(x) as 8 8 1 > > (12x þ 15x2 þ 16x4 4x6 ) 0,x > > 161 2 > < 31 32 1 2 3 6 FX (x) ¼ þ (6x þ 3x 2x x ) ,x1 > 161 161 2 > > > x.1 > :1 0 otherwise Similarly, we integrate fXY (x,y) over all x to obtain the marginal density fY ( y) as shown below: 8 ð pyffiffi 192 1 > > > (1 þ 2xy)dx, 0,y < 161 2 fY ( y) ¼ ð 0pyffiffi > 192 1 > > (1 þ 2xy)dx, ,y1 : 161 2 y1=2 or 8 192 pffiffiffi > > y y2 > < 161 pffiffiffi fY ( y) ¼ 16 6 þ 12 y 15y þ 24y2 12y3 > > > 161 : 0
0,y
1 2
1 ,y1 2 otherwise
9.2
Joint Continuous Distribution Functions
143
Integrating fY ( y) in the above regions we obtain the distribution function FY ( y) as shown below: 8 64 3=2 > > 2y þ y3 > > 161 > < 1 16 FY ( y) ¼ þ (6y þ 8y3=2 15y2 þ 8y3 3y4 ) > 7 161 > > > > :1 0
0,y
1 2
1 ,y1 2 y.1 otherwise
fX(x), FX (x), fY ( y) and FY ( y) are shown in Fig. 9.2.6. Method 2: Marginal Distributions from FXY (x,y). We will now find the marginal distributions from the joint distribution FXY (x,y) obtained by integrating the joint density fXY (x,y) over the regions of definition. The joint distribution FXY (x,y) in the region 0 , x 12, 0 , y 12 is obtained from the following integration: ðx ðy FXY (x,y) ¼ 0
x2
192 (1 þ 2jh)dh dj 161
32 ¼ 6xy 2x3 þ 3x2 y2 x6 , 161
0,x1 0 , y 1=2
and in the region 0 , x 1, 12 , y 1, we have ð y(1=2) ð jþ(1=2) FXY (x,y) ¼ 0
ðx
j2
ðy
þ y(1=2) j
2
192 (1 þ 2jh)dh dj 161
192 (1 þ 2jh)dh dj 161
or FXY (x,y) ¼
1 8 þ 12y 15y2 þ 8y3 6y4 7 161 2 2
3
þ 24xy þ 12x y 8x 4x
FIGURE 9.2.6
6
(
0,x,1 1 ,y1 2
144
Joint Densities and Distributions
The marginal distributions FX (x) and FY ( y) can be obtained by substituting suitable boundary values in the joint distribution FXY (x,y) as shown below:
FX (x) ¼
8 1 > > F x,x þ , > XY > > 2 > > > > > < FXY (x,1), > > > FXY (1,1), > > > > > > > : FXY (0,0),
1 1 0,x , y. 2 2 1 1 , x 1, y . 2 2 1 x . 1, y . 2 1 x 0, y . 2
Similarly 8 pffiffiffi > > FXY ( y,y), > > > < pffiffiffi FY (y) ¼ FXY ( y,y), > > > > > : FXY (1,1), FXY (0,0),
1 0,y , 0,x1 2 1 , x 1, 0 , x 1 2 y . 1, x . 1 y 0, x . 1
and these are exactly the same as derived previously.
B 9.3
BIVARIATE GAUSSIAN DISTRIBUTIONS
If X and Y are jointly distributed random variables with mean value mX and mY and variances s2X and s2Y, then the joint density function fXY (x,y) is given by ( " 1 1 x mX 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi exp fXY (x,y) ¼ 2(1 r2 ) sX 2psX sY 1 r2 #) 2r(x mX )(y mY ) y mY 2 þ (9:3:1) sX sY sY where r is the correlation coefficient that determines the degree of similarity between X and Y, given by sXY r¼ (9:3:2) sX sY We have to show that ðð 1 I¼
FXY (x,y)dy dx ¼ 1
(9:3:3)
1
Substituting j ¼ (x 2 mX)/sx and h ¼ ( y 2 mY)/sy in Eq. (9.3.1), we can write the integral I as follows: 1 1 2 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi exp ½j 2rjh þ h dh dj 2(1 r2 ) 1 2p 1 r2
ðð 1 I¼
(9:3:4)
145
9.3 Bivariate Gaussian Distributions
Completing the squares in j in the integral (9.3.4) above, we have
ðð 1 1 1 (j rh)2 pffiffiffiffiffiffiffiffiffiffiffiffiffi exp I¼ þ h dh dj 2 1 r2 1 2p 1 r2
(9:3:5)
Substituting j rh w ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 1 r2 and dj dw ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 1 r2 the integral (9.3.5) can be written as follows: 2 2 ð1 ð 1 w 1 1 h pffiffiffiffiffiffi exp exp I¼ dw dh ¼ 1 2p 1 2 2 2p 1
(9:3:6)
If r ¼ 0 in Eq. (9.3.3), then X and Y are independent because fXY (x,y) ¼ fX (x) fY ( y). Thus, if Gaussian-distributed random variables X and Y are uncorrelated, they are also independent. We shall find the conditional pdf fY j X (X ¼ x) given X ¼ x. From Eq. (8.2.5) we have fXY (x,y) fX (x) 8 9 1 1 x mX > > > > pffiffiffiffiffiffiffiffiffiffiffiffiffi exp > > > > 2 > > 2(1 r2 ) sX 1 r 2ps s > > X Y > > > >
> > 2 > > > > (2r(x m )( y m ) y m X Y Y > > > > þ = < sX sY sY ¼ 2 > > 1 1 x mX ) > > > > pffiffiffiffiffiffi exp > > > > > > 2 s 2p s X > > X > > > > > > > > > > > > ; :
fY j X (y j X ¼ x) ¼
( " 2 ) 1 1 rsY ¼ pffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi exp (x mX Þ y mY sX 2s2Y (1 r2 ) 2psY 1 r2 Thus the conditional density has a conditional mean value of rsY mY j X ¼ mY þ (x mX ) sX and a conditional variance of s2Y j X ¼ s2Y (1 r2 ):
CHAPTER
10
Moments and Conditional Moments
B 10.1
EXPECTATIONS
The expected value or the mean value of any random variable X is defined by ð1 E ½X ¼ mX ¼ x fX (x) dx (10:1:1) 1
This definition is valid regardless of whether the random variable is continuous or discrete. Since the pdf of a discrete random variable is always given by a sum of delta functions, we have ) ð 1 (X 1 E½X ¼ mX ¼ x P{X ¼ xi }d(x xi ) dx 1
¼
1 X
i¼0
xi P{X ¼ xi }
(10:1:2)
i¼0
The expected value is the first moment of the random variable X about the origin. Example 10.1.1
For a random variable uniformly distributed in the interval (a,b], we have fX (x) ¼
1 ba
and hence the mean value is given by
E½X ¼
1 ba
ðb x dx ¼ a
b 1 x2 1 b2 a2 b þ a ¼ ¼ b a 2 a 2 b a 2
Example 10.1.2 On a toss of a die, a person wins $10 if {1} or {3} results, loses $5 if {4} or {6} results, wins $5 if {2} results, and loses $10 if {5} results. From Eq. (10.1.2), Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
146
10.1 Expectations
147
the expected value of the winnings is 6 X
E{X} ¼ mX ¼
xi P{X ¼ xi }
i¼1
1 5 ¼ {10 þ 10 5 5 þ 5 10} ¼ 6 6 The expected value of any random variable is a constant. In other words, it smooths the variations of a random variable to a constant. It is a one-way operator, in the sense that once the random variable X is smoothed, there is no way of getting back the density function. Thus, the expected value of a constant a is the same constant a.
Properties of Expectations 1. It is a linear operator irrespective of the nature of random variables, in the sense that the expected value of the sum equals the sum of expected values E½X þ Y ¼ E½X þ E½Y
(10:1:3)
as shown below: ð1 ð1 E½X þ Y ¼
(x þ y)fXY (xy)dx dy 1 1 ð1 ð1
ð1 ð1 xfXY (xy)dx dy þ
¼ 1 1 ð1
ð1
xfX (x)dx þ
¼ 1
yfXY (xy)dx dy 1 1
yfY (y)dy ¼ E{X} þ E{Y} 1
2. On the contrary, the expected value of the product given by ð1 ð1 E½XY ¼ xyfXY (xy)dx dy
(10:1:4)
1 1
is not, in general, equal to the product of expected values. However, Equation (10.1.4) can be split only if X and Y are independent, in which case we have ð1 ð1 E½XY ¼ xy fXY (xy)dx dy 1 1
ð1
ð1
xfX (x)dx
¼ 1
yfY (y)dy 1
¼ E½X E½Y and the expected value of the product is equal to the product of expected values. The converse is not true. If E[XY ] ¼ E[X ] E[Y ], then X and Y are uncorrelated and need not be independent. Analogously, if E[XY ] ¼ 0, then X and Y are orthogonal. We can summarize the following definitions: Uncorrelated: E[XY ] ¼ E[X ] E[Y ] Orthogonal: E[XY ] ¼ 0 Independence: fXY (xy) ¼ fX (x) fY (y)
148
Moments and Conditional Moments
Conditional Expectation
Given that the random variable X has occurred, the expected value of Y is defined by ð1 E½Y j X ¼ yfYjX ( y j x)dy (10:1:5) 1
The conditional expectation operator does not completely smooth the random variable Y to a constant but smooths it to random variable dependent on X. Thus, if we take the expectation of the conditional expectation, we get the expected value of Y: ð 1 ð 1 E{E½Y j X} ¼ y fY j X ( y j x) dy fX (x)dx 1
1
ð1
y fXY (xy) dy dx
¼ 1 ð1
y fY ( y)dy ¼ E½Y
¼
(10:1:6)
1
If X and Y are independent, then E[Y j X ] ¼ E[Y ] since fY j X ( y j x) ¼ fY ( y).
Example 10.1.3 A random variable Y is uniformly distributed in the interval (0,5}. Hence the distribution function FY( y) is given by y/5 for 0 , y 5. We will find the conditional expectation of Y given that Y . x and 0 , x 5. We first find the conditional distribution FY ( y j Y . x): FY ( y j Y . x) ¼
P(x , Y y) FY ( y) FY (x) ¼ P(Y . x) 1 FY (x)
Differentiating this equation with respect to y, the conditional density fY( y j Y . x) is obtained as follows: fY (y) ¼
fY ( y) 1 ¼ 1 FY (x) 5 x
Hence the conditional expectation E[Y j Y . x] is given by 5 y y2 E½Y j Y . x ¼ dy ¼ 2(5 x)x x5 x ð5
¼
25 x2 5þx ¼ 2 2(5 x)
Joint Moment
We can now define the joint moment between two random variables X and Y as follows: ð1 ð1 E½XY ¼ mXY ¼ xy fXY (x,y) dx dy (10:1:7) 1
1
The joint moments determine the correlation between X and Y.
10.2
B 10.2
Variance
149
VARIANCE
The variance is the second moment about the mean value also known as the second central moment. It is defined by ð1 var½X ¼ E½X mX 2 ¼ s2X ¼ (x mX )2 fX (x) dx 1 2
¼ E½X 2mX E½X þ
m2X
¼ E½X 2 m2X
(10:2:1)
The variance expresses the variability of the random variable about the mean value. If this variability is zero, then the random variable will be no longer random but a constant. This will be discussed further in Chapter 14. The preceding definition applies for any random variable. However, for a discrete random variable, analogous to Eq. (10.1.2), we have E½X mX 2 ¼ sX2 ¼
1 X
(xi mX )2 P{X ¼ xi )
(10:2:2)
i¼0
Example 10.2.1 For a random variable, X uniformly distributed between (a,a, the mean value mX ¼ 0. Hence the variance is given by ð 1 a 2 a2 2 2 sX ¼ E½X ¼ x dx ¼ 2a a 3 Example 10.2.2 From Eq. (10.2.2), the variance of the random variable given in Example 10.1.2 is 6 X 5 21 s2X ¼ xi 6 6 i¼1 2 1 55 552 252 252 252 552 10,950 ¼ þ þ þ þ þ ¼ 6 62 216 62 62 62 62 62 Properties of Variance 1. 2.
var½aX ¼ a2 var½X ¼ a2 s2X
(10:2:3)
var½X + Y ¼ E½X + Y2 {E½X þ E½Y}2 ¼ E½X 2 {E½X}2 þ E½Y 2 {E½Y}2 + 2{E½XY E½XE½Y} ¼ sX2 þ s2Y + 2{E½XY E½XE½Y}
(10:2:4)
3. If X and Y are independent, then var½X + Y ¼ s2X þ s2Y
(10:2:5)
Conditional Variance. We can also define a conditional variance of Y given X has occurred as var½Y j X ¼ E½(Y mYjX )2 j X
(10:2:6)
150
Moments and Conditional Moments
Covariance. The covariance between two random variables X and Y is defined by cov½XY ¼ sXY ¼ E ½X mX E ½Y mY ¼ E ½XY mX mY
(10:2:7)
If X and Y are independent, then cov[XY ] ¼ 0. The converse is not true. If cov[XY ] ¼ 0, then X and Y are uncorrelated but not necessarily independent. Correlation Coefficient. The correlation coefficient is the normalized covariance and is defined by cov½XY sXY rXY ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ var½X var½Y sX sY
(10:2:8)
The correlation coefficient satisfies 1 rXY 1 and shows the degree of selfsimilarity between X and Y.
B 10.3 MEANS AND VARIANCES OF SOME DISTRIBUTIONS Discrete Random Variables Binomial Distribution
Mean: n k nk b(k; n, p) ¼ pq k E½X ¼
n X k¼0
¼
k
(10:3:1)
n! pk qnk k!(n k)!
n X
n(n 1)! p pk1 qnk (k 1)!(n k)! k¼0
¼ np
n X
(n 1)! pk1 qnk (k 1)!(n k)! k¼1
¼ np( p þ q)n1 ¼ np
(10:3:2)
Variance: var½X ¼ E½X 2 m2X E½X 2 ¼
n X k¼0
k2
n! pk qnk k!(n k)!
(10:3:3) (10:3:4)
Substituting k 2 ¼ k (k 2 1) þ k in Eq. (10.3.4), we have E½X 2 ¼
n X k¼0
k(k 1)
n X n! n! pk qnk þ pk qnk k k!(n k)! k!(n k)! k¼0
(10:3:5)
10.3
Means and Variances of Some Distributions
151
The second summation in Eq. (10.3.5) is the mean value, already calculated as np in Eq. (10.3.2). The first summation can be written as follows: n X
k(k 1)
k¼0
n X n! (n 2)! pk qnk ¼ n(n 1) p2 pk2 qnk k!(n k)! (k 2)!(n k)! k¼2
¼ n(n 1) p2 (p þ q)n2 ¼ n(n 1)p2
(10:3:6)
Thus E½X 2 ¼ n (n 1) p2 þ np
(10:3:7)
and the variance from Eq. (10.3.3) is given by var½X ¼ n (n 1) p2 þ np n2 p2 ¼ npq
(10:3:8)
Poisson Distribution
Mean: P(k; l) ¼ el E½X ¼ el
lk k! 1 X klk k¼0
k!
(10:3:9) ¼ lel
1 X lk 1 ¼ l el el ¼ l (k 1)! k¼1
(10:3:10)
Variance. We use the same technique as in the binomial distribution by substituting k 2 ¼ k(k 2 1) þ k. Hence E½X 2 ¼ el
1 X k(k 1)lk k¼0
¼ l2 el
k!
þ el
1 X klk k¼0
k!
1 X lk 2 þl (k 2)! k¼2
¼ l2 el el þ l ¼ l2 þ l
(10:3:11)
Substituting in var½X ¼ E½X 2 m2X from Eqs. (10.3.11) and (10.3.10), we have var ½X ¼ l2 þ l l ¼ l
(10:3:12)
For a Poisson distribution, the mean and the variance are the same and equal to l. Continuous Random Variables Exponential Distribution
Mean: fX (x) ¼ l elx u(x) ð1 E½X ¼ l xelx dx 0
(10:3:13) (10:3:14)
152
Moments and Conditional Moments
Integrating Eq. (10.3.14) by parts, we obtain ð1 ð1 delx ¼ E½X ¼ l x x delx l 0 0 1 ð 1 1 lx ¼ xe þ delx ¼ l 0 0 Variance. We first calculate the second moment: ð1 E½X 2 ¼ l x2 elx dx
(10:3:15)
(10:3:16)
0
Integrating by parts [Eq. (10.3.16)] twice, we get the result as ð1 l
x2 elx dx ¼
0
lx 1 e 2 2 ¼ 2 (l x þ 2lx þ 2) 2 l2 l 0
(10:3:17)
2 1 1 ¼ l2 l2 l2
(10:3:18)
and the variance is given by var½X ¼
Gaussian Distribution
Mean: 1 1 (x mX )2 pffiffiffiffiffiffi exp 2 s2X sX 2p ð1 1 1 (x mX )2 x exp dx E½X ¼ pffiffiffiffiffiffi 2 s2X sX 2p 1 ð1 1 1 (x mX )2 ¼ pffiffiffiffiffiffi (x mX ) exp dx 2 s2X sX 2p 1 ð1 m 1 (x mX )2 exp dx þ pXffiffiffiffiffiffi 2 s2X sX 2p 1
fX (x) ¼
(10:3:19)
(10:3:20)
In Eq. (10.3.20), the first integral is zero because it is an odd function and the second integral equals mX. Hence E½X ¼ mX
(10:3:21)
Variance: 1 var½X ¼ pffiffiffiffiffiffi sX 2p
1 (x mX )2 (x mX ) exp dx 2 s2X 1
ð1
2
Substituting y ¼ (x mX )=sX in Eq. (10.3.22), we have ð 1 1 2 2 (1=2)y2 sX y e dy ¼ s2X var½X ¼ pffiffiffiffiffiffi 2p 1 pffiffiffiffiffiffi Ð 1 2 where it can be shown that 1= 2p 1 y2 e(1=2)y dy ¼ 1
(10:3:22)
(10:3:23)
10.4
B 10.4
Higher-Order Moments
153
HIGHER-ORDER MOMENTS
We can now define higher-order moments. The kth-order moment is defined by E½X k ¼ mk ¼
ð1
xk fX (x) dx
(10:4:1)
1
Clearly, m0 ¼ 1 and m1 ¼ mX. Similarly, the kth-order central moments are defined by E½X
mkX
ð1
(x mkX ) fX (x) dx
¼ vk ¼
(10:4:2)
1
Here v0 ¼ 1, v1¼0, and v2 ¼ s2X. We can find relationships between the kth-order moments and the kth-order central moments. We can write these relationships as follows: v0 ¼ 1 v1 ¼ 0 v 2 ¼ s2 v3 ¼ m3 3m1 m2 þ 2m31
m0 ¼ 1 m1 ¼ m m2 ¼ v2 þ m2 m3 ¼ v3 þ 3m1 v2 þ m31
(10:4:3)
The moments of random variables are not arbitrary numbers but must satisfy certain inequalities. For example, since E½X mX 2 must be nonnegative, we have s2 ¼ m2 m21 0
and
m2k m2k 0
for all k
(10:4:4)
A random variable is completely determined if the density function is known. However, knowledge of only two moments gives only a limited information of a random variable. But under certain conditions, knowledge of moments mk for all k will uniquely determine the random variable. However, because only two of the moments of a Gaussian random variable are independent, it can be completely determined with two moments. All other moments are dependent on two moments. We will show that for a Gaussian random variable with density function 1 2 2 fX (x) ¼ pffiffiffiffiffiffi ex =2s s 2p satisfies the following moment equations: 0 k E½X ¼ 1 3 5 (n 1)sn
n ¼ 2k þ 1 for all k n ¼ 2k for all k
(10:4:5)
(10:4:6)
function. To show the result for Clearly the odd moments are 0 because fXÐ(x) is an even p ffiffiffiffiffiffiffiffi 2 1 the even moments, we take the equation 1 ebx dx ¼ p=b and differentiate it k time with respect to b, yielding rffiffiffiffiffiffiffiffiffiffiffi ð1 1 3 5 (2k 1) p 2k bx2 x e dx ¼ (10:4:7) k 2kþ1 2 b 1 and the result is obtained by substituting b ¼ 1/2s2 in Eq. (10.4.7). Thus, the Gaussian random variable is completely determined if only two moments are known.
154
Moments and Conditional Moments
Higher-Order Cross-Moments
The concept of joint moments can be extended to higher-order cases. The k, jth higher-order cross-moment is defined by, ð1 ð1 k j E½X , Y ¼ mkj ¼ xk y j fXY (x,y) dx dy (10:4:8) 1
1
and the k, jth higher order central moment is defined by ð1 ð1 k j E½(X mX ) , (Y mY ) ¼ hkj ¼ (x mX )k ( y mY ) j fXY (x,y) dx dy 1
(10:4:9)
1
and m11 ¼ E [XY ] the joint moment and h11 ¼ cov[XY ] ¼ sXY is the covariance.
B 10.5
BIVARIATE GAUSSIAN
Two random variables are jointly Gaussian-distributed if they satisfy the following density function with r as the correlation coefficient: ( " 1 1 x mX 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi exp fXY (x,y) ¼ 2(1 r2 ) sX 2psX sY 1 r2 #) (x mX )(y my ) y mY 2 þ (10:5:1) 2r sY s X sY Ordinarily, uncorrelatedness does not imply independence, even though independence implies uncorrelatedness. However, in the case of Gaussian-distributed random variables if r ¼ 0, then Eq. (10.5.1) becomes ( " #) 1 1 x mX 2 y mY 2 fXY (x,y) ¼ exp þ (10:5:2) 2psX sY 2 sX sY and clearly fXY (x,y) ¼ fX (x) fY (y) ( ( ) ) 1 1 x mX 2 1 1 x mY 2 pffiffiffiffiffiffi exp ¼ pffiffiffiffiffiffi exp 2 2 sX sY 2psX 2psY and hence X and Y are independent.
(10:5:3)
CHAPTER
11
Characteristic Functions and Generating Functions
B 11.1
CHARACTERISTIC FUNCTIONS
The characteristic function (CF) of any random variable X (discrete or continuous) is defined by ð1 FX (v) ¼ E{e jvX} ¼ e jvx fX (x) dx (11:1:1) 1
From this definition it is seen that the characteristic function is the Fourier transform of the pdf fX (x) where the sign of v is positive instead of negative. However, all the properties of the Fourier transform hold good. From the definition of FX (v), we can see that FX (0) ¼ 1
(11:1:2)
This equation is used to check the correctness of the CF. Existence of CF
The CF is bounded as shown below. Applying the fact that the absolute value of an integral is less than or equal to the integral of its absolute value, we have ð1 ð 1 jvx e fX (x) dx je jvx jj fX (x)j dx 1
and since je
jvx
1
j ¼ 1 we have ð1 ð 1 jvx e fX (x) dx je jvx jj fX (x)j dx 1 1 ð1 ¼ j fX (x)jdx ¼ 1 1
Thus, the CF always exists.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
155
156
Characteristic Functions and Generating Functions
We can also obtain the pdf fX(x) from the CF from the inversion formula for the Fourier transform given by ð 1 1 FX (v)ejvx dv (11:1:3) fX (x) ¼ 2p 1 [Comment: Since FX(v) is the Fourier transformation of the pdf fX(x), the random variable X is also completely determined if FX(v) is known. A Fourier transform of a signal characterizes the frequency content of the signal. No such characterization for the CF can be given; it is just a convenient form of representing the pdf and has other very useful properties.] Example 11.1.1
We will find the CF of an exponential distribution given by fX (x) ¼ lelx u(x)
Using Eq. (11.1.1), we have ð1 FX (v) ¼
lelx e jvx dx ¼
0
l l jv
Moment Generating Properties of CF
In Chapter 10 we found the moments of a random variable using integration techniques. Once the CF is known, the moments can be obtained by differentiation, which is a simpler process than integration. Expanding the CF as a Taylor series about the origin, we can write ð1 jvx ( jvx)2 (jvx)3 ( jvx)k þ FX (v) ¼ þ þ þ þ dv (11:1:4) fX (x) 1 þ 1! 2! 3! k! 1 Substituting ð1 mk ¼
xk fX (x) dx,
k ¼ 0,1; . . .
(11:1:5)
1
in Eq. (11.1.2), we have jvm1 ( jv)2 m2 ( jv)3 m3 ( jv)k mk FX (v) ¼ 1 þ þ þ þ þ þ 1! 2! 3! k! We now differentiate FX(v) term by term to yield d jm1 2( j)2 vm2 3( j)3 v2 m3 k( j)k vk1 mk FX (v) ¼ þ þ þ þ þ dv 1! 2! 3! k!
(11:1:6)
Substituting v ¼ 0 in Eq. (11.1.6), we get F0X (v)v¼0 ¼ jm1
and m1 ¼
F0X ð0Þ j
(11:1:7)
Differentiating FX(v) successively k times and substituting in the resulting equations v ¼ 0 we can obtain all the moments of the random variables as shown below: (k) 2 k F(2) (11:1:8) X (v) v¼0 ¼ ( j) m2 , . . . , FX (v) v¼0 ¼ ( j) mk , . . . , We will give more examples in a later section.
11.2
Examples of Characteristic Functions
157
Joint Characteristic Functions
We can also define a joint characteristic function for two random variables X and Y as FXY (v1 , v2 ) ¼ E e j(v1 Xþv2 Y) ð1 ð1 ¼ fXY (x, y)e j(v1 xþv2 y) dx dy
(11:1:9)
1 1
B 11.2
EXAMPLES OF CHARACTERISTIC FUNCTIONS
We will now give several examples of characteristic functions for both discrete and continuous random variables and determining their means and variances. Discrete Random Variables
Example 11.2.1 (Binomial Distribution) n k nk pq b(k: n,p) ¼ k
(11:2:1)
The CF is given by n X n k nk jvk pq e k¼0 k n X n ¼ pe jvk qnk ¼ ( pe jv þ q)n k k¼0
FX (v) ¼
(11:2:2)
We find that FX(0) ¼ ( p þ q) ¼ 1 Mean: F0X (v) ¼ n( pe jv þ q)n1 jpe jv F0X (0) ¼ jnp
or E{X ¼ k} ¼ m1 ¼ np
(11:2:3)
Variance. We first find EfX 2 ¼ k 2g ¼ m2: F00X (v) ¼ n(n 1)( pe jv þ q)n2 ( jpe jv )2 þ n( pe jv þ q)n1 j2 pe jv F00X (0) ¼ n(n 1)p2 np and
m2 ¼ (np)2 þ npq
Hence var½X ¼ m2 m21 ¼ npq
(11:2:4)
158
Characteristic Functions and Generating Functions
Example 11.2.2 (Poisson Distribution) p(k; l) ¼ el FX (v) ¼
1 X
lk k! el
lk jvk e k!
el
le jv jv jv ¼ el ele ¼ el(1e Þ k!
k¼0
¼
1 X
(11:2:5)
k
k¼0
(11:2:6)
and FX(0) ¼ 1. Mean: jv
F0X (v) ¼ el(1e ) l(e jv )j
(11:2:7)
F0X (0) ¼ lj or E{X ¼ k} ¼ m1 ¼ l Variance. We first find EfX 2 ¼ k2g ¼ m2: jv
jv
F00X (v) ¼ el(1e ) l(e jv )j l(e jv )j el(1e ) lj(e jv )j F00X (0) ¼ l2 l or
m2 ¼ l2 þ l
Hence var{X} ¼ m2 m21 ¼ l
(11:2:8)
Example 11.2.3 (Geometric Distribution) p{k : p} ¼ P{k failures followed by a success} ¼ qk p FX (v) ¼
1 X
pqk e jvk ¼ p
k¼0
1 X
(11:2:9)
(qe jv )k
k¼0
¼
p 1 qe jv
(11:2:10)
and FX(0) ¼ 1. Mean: F0X (v) ¼
p jpqe jv jv (jqe ) ¼ (1 qe jv )2 (1 qe jv )2
F0X (0) ¼
jpq p2
or E{x ¼ k} ¼ m1 ¼
q p
(11:2:11)
Variance. We first find EfX 2 ¼ k 2g ¼ m2. F00X (v) ¼ F00X (0)
2jpqe jv j2 pqe jv jv (jqe ) þ (1 qe jv )3 (1 qe jv )2
2pq2 pq ¼ 2 p p3
2q2 q or m2 ¼ 2 þ p p
(11:2:12)
11.2
Examples of Characteristic Functions
159
Hence variance is m2 m2X ¼
2q2 q q2 q(p þ q) q þ 2¼ ¼ 2 p p p2 p p2
(11:2:13)
Continuous Random Variables
Example 11.2.4 (Uniform Distribution) 1 ½u(x b) u(x a) ba ð b jvx e e jvb e jva FX (v) ¼ dx ¼ jv(b a) aba fX (x) ¼
(11:2:14) (11:2:15)
This is one of the more difficult CFs to obtain the mean and the variance by differentiating Eq. (11.2.15) because of the limiting processes involved. Instead, we expand Eq. (11.2.15) using Taylor series and differentiate the resulting expression. Expansion of Eq. (11.2.15) results in 1 ( jv)2 2 ( jv)3 3 FX (v) ¼ jv(b a) þ (b a2 ) þ (b a3 ) þ jv(b a) 2! 3! jv ( jv)2 2 (b þ a) þ (b þ ab þ a2 ) þ ¼ 1þ (11:2:16) 2! 3! Mean: F0X (v)
j 2j2 v 2 2 (b þ a) þ (b þ ab þ a ) þ ¼ 2! 3!
F0X (0)
j(b þ a) ¼ 2
(11:2:17)
(b þ a) or m1 ¼ 2
Variance: F00X (v) ¼ F00X (0) ¼
2j2 2 (b þ ab þ a2 ) þ 3!
(b2 þ ab þ a2 ) 3
or
m2 ¼
b2 þ ab þ a2 3
Hence var½X ¼ m2 m21 ¼
(b a)2 12
(11:2:18)
In most cases it is easier to find the mean and variance using CF. However, in this case it is easier to obtain mean and variance by integration techniques using their definitions.
Example 11.2.5 (Exponential Distribution) fX (x) ¼ lelx u(x)
(11:2:19)
160
Characteristic Functions and Generating Functions
From Example 11.1.1 the CF is ð1 FX (v) ¼
lelx e jvx dx ¼
0
l l jv
(11:2:20)
Mean: F0X (v) ¼ F0X (0)
jl (l jv)2
j ¼ l
(11:2:21)
1 m1 ¼ l
or
Variance: F00X (v) ¼
2l (l jv)3
F00X (0) ¼
2 l2
or
m2 ¼
(11:2:22)
2 l2
Hence var½X ¼ m2 m21 ¼
1 l2
(11:2:23)
Example 11.2.6 (Gaussian Distribution) 2 1 2 fX (x) ¼ pffiffiffiffiffiffi e(xm) =2s 2p s ð1 2 1 2 FX (v) ¼ pffiffiffiffiffiffi e(xm) =2s þjvx dx 2p s 1
(11:2:24) (11:2:25)
Completing the squares in the exponent of Eq. (11.2.25), we obtain the resulting expression as follows: ð1 1 (x m jvs2 )2 jvm½(s2 v2 )=2 p ffiffiffiffiffiffi FX (v) ¼ e exp dx 2s2 2p s 1 The value of the integral within braces is 1 and hence the CF of a Gaussian is 2
FX (v) ¼ e jvm½(s v
2
)=2
(11:2:26)
Mean: 2
2
F0X (v) ¼ e jvm½(s v )=2 ( jm s2 v) F0X (0) ¼ jm or m1 ¼ m
(11:2:27)
Variance: 2
F00X (v) ¼ e jvm½ðs v
2
F00X (0) ¼ m2 s2
Þ=2
( jm s2 v)2 e jvm½(s
or m2 ¼ m2 þ s2
2
v2 )=2 2
s
11.3 Generating Functions
161
Hence var½X ¼ m2 m21 ¼ s2
B 11.3
(11:2:28)
GENERATING FUNCTIONS
Certain simplifications are possible in the definition of the characteristic functions for discrete random variables taking positive integral values. This is called the generating function (GF) and is defined for a discrete random variable X taking positive discrete values as PX (z) ¼ E{zk } ¼
1 X
zk P{X ¼ k}
(11:3:1)
k¼0
with PX(1) ¼ 1. The infinite series of Eq. (11.3.1) will converge for values of jzj , 1. Equation (11.3.1) can be recognized as the z transform of discrete data with the sign reversal for z. Example 11.3.1 Six dice are tossed. Using the generating function, we want to find the probability that the sum of the faces of the dice add to 24. The GF for the toss of a single die is 1 PX (z) ¼ ½z þ z2 þ z3 þ z4 þ z5 þ z6 6 and in closed form this can be written as z 1 z6 PX (z) ¼ 6 1z Since the tosses are independent, the GF for the toss of 6 dice is [PX(z)]6 given by QX (z) ¼ ½PX (z)6 ¼
z 6 1 z6 6 6
1z
To find Pffaces add up to 24g, we have to first find the coefficient of z 24 in the power series expansion of Q(z). Alternately, we have to find the coefficient of z 18 in the expansion of RX (z) ¼
(1 z6 )6 (1 z)6
The expansion of the numerator of RX(z) is (1 z)6 ¼ 1 6z6 þ 15z12 20z18 þ 15z24 6z30 þ z36 and the expansion of the denominator of RX(z) is given by the negative exponential (1 z)6 ¼
1 X (5 þ k 1)! k¼1
5!(k 1)!
zk1
162
Characteristic Functions and Generating Functions
We now multiply the two expansions above and find the coefficient z 18 in the resulting product. The terms containing z 18 are (5 þ 7 1) (5 þ 13 1)! (5 þ 19 1)! 6 þ1 z18 20 þ 15 5!6! 5!12! 5!18! ¼ 3451z18 Hence the probability that the sum of the faces add to 24 is P{sum ¼ 24} ¼
3451 ¼ 0:073967 66
a result that would have been very difficult to obtain without generating functions.
Moment Generating Properties of GF
It is easier to use the GF in finding the moments of a discrete random variable taking positive integral values than finding it through CF. We can differentiate Eq. (11.3.1) and obtain PX (z) ¼
1 X
zk P{X ¼ k}
k¼0
P0X (z) ¼
1 X
kzk1 P{X ¼ k}
(11:3:2)
k¼0
P0X (1) ¼
1 X
kP{X ¼ k} ¼ m1
k¼0
Differentiating PX0 (z) again, we obtain P00X (z) ¼
1 X
k(k 1)P{X ¼ k}
k¼0
¼
1 X
k2 P{X ¼ k}
k¼0
1 X
kP{X ¼ k}
(11:3:3)
k¼0
P00X ð1Þ ¼ m2 m1 Hence the variance is given by
2 var½X ¼ P00X (1) þ P0X (1) P0X (1)
(11:3:4)
Unlike the CF, there is no general formula for the kth moment in the case of GF. In particular P000 X (1) ¼ m3 3m2 þ 2m1
B 11.4
(11:3:5)
EXAMPLES OF GENERATING FUNCTIONS
We will now derive generating functions for all discrete random variables discussed in Section 11.2 and determine their means and variances.
11.4 Examples of Generating Functions
163
Example 11.4.1 (Binomial Distribution) b(k: n,p) ¼
n k nk pq k
(11:4:1)
The GF is given by N X n PX (z) ¼ pzk qnk ¼ (pz þ q)N k k¼0
(11:4:2)
Clearly PX(1) ¼ 1. Mean: P0X (z) ¼ N(pz þ q)N1 p P0X (1) ¼ Np
or m1 ¼ Np
(11:4:3)
Variance: P00X (z) ¼ N(N 1)(pz þ q)N2 p2 P00X (1) ¼ N(N 1)p
or m2 ¼ N(N 1)p2 Np
Hence var½X ¼ m2 m21 ¼ Npq
(11:4:4)
This result has been obtained with comparative ease. Example 11.4.2 (Poisson Distribution) p(k; l) ¼ el
lk k!
(11:4:5)
The GF is given by PX (z) ¼
1 X
el
k¼0
lzk ¼ el(1z) k!
(11:4:6)
Note that PX(1) ¼ 1. Mean: P0X (z) ¼ el(z1) ¼ el(z1) l P0X (1) ¼ l or m1 ¼ l
(11:4:7)
a result that has been trivially obtained compared to Eq. (11.2.7). Variance: P00X (z) ¼ el(z1) l2 P00X (1) ¼ l2
or
m2 ¼ l2 l
(11:4:8)
Hence var½X ¼ m2 m21 ¼ l A result that has been obtained much more easily than using Eq. (11.2.8).
(11:4:9)
164
Characteristic Functions and Generating Functions
Example 11.4.3 (Geometric Distribution) p{k: p} ¼ qk p
(11:4:10)
The GF is PX (z) ¼
1 X
qzk p ¼
k¼0
p 1 qz
(11:4:11)
Here also PX(1) ¼ 1. Mean: pq (1 qz)2 q P0X (1) ¼ p P0X (z) ¼
(11:4:12)
Variance: P00X (z) ¼ 2 P00X (1)
pq2 (1 qz)3
pq2 2q2 ¼2 ¼ p2 (1 qz)3
(11:4:13)
or m2 ¼
2q2 q þ p p2
Hence var½X ¼ m2 m21 ¼
B 11.5
q p2
(11:4:14)
MOMENT GENERATING FUNCTIONS
The moment generating function (MGF) MX(t) if it exists is defined by ð1 MX (t) ¼ E(etX ) ¼ fX (x)etx dx
(11:5:1)
1
where t is a real variable. This is similar to the characteristic function (CF) [Eq. (11.1.1)], except that jv has been replaced by the real variable t. Unlike the CF, which always exists, the MGF may or may not exist. Expanding E(e tX) in a power series about the origin and taking expectations, if they exist, we can write t2 X 2 tn X n tX þ þ þ MX (t) ¼ E(e ) ¼ E 1 þ tX þ 2! n! ¼ 1 þ tm þ
t2 m2 t n mn þ þ þ 2! n!
(11:5:2)
11.5
Moment Generating Functions
165
If MX(t) exists, Eq. (11.5.2) can be differentiated term by term to obtain d tm2 tn1 mn MX (t) ¼ m þ þ þ þ dx 2! n! Substituting t ¼ 0 in Eq. (11.5.3), we obtain MX0 (t)t¼0 ¼ m
(11:5:3)
(11:5:4)
Differentiating Eq. (11.5.2) successively with respect to t and setting t ¼ 0 in the resulting equation, we obtain the moments as follows: MX(2) (t)t¼0 ¼ m2 , . . . , m(n) (11:5:5) X (t)jt¼0 ¼ mn , . . . These results are very similar to those in Eq. (11.1.5) except for the absence of pffiffiffiffiffiffi ffi j ¼ 1. Since the methodology of finding the moments using MGF is very similar to that of using CF, we will give only three examples. Example 11.5.1 (Negative Binomal—Pascal) pmf is
If N is the random variable, then the
nb{n; k,p} ¼ P{n trials produce k successes} ¼
n 1 k nk p q : n ¼ k, k þ 1, . . . k1
Or substituting m ¼ n 2 k, we can write the pmf with M as the random variable: nb{m; k, p} ¼ P{(m þ k) trials produce k successes} kþm1 k m p q , m ¼ 0, 1, . . . ¼ k1 Since
kþm1 k1
¼
kþm1 m
we can write the MGF for the random variable M: 1 X kþm1 MM (t) ¼ (qet )m pk m m¼0 The negative binomial expansion of (1 2 qe t)2k can be written as follows: (1 qet )k ¼
1 X kþm1 (qet )m m m¼0
Substituting this equation in the previous one, we obtain the MGF: MM (t) ¼ pk (1 qet )k Clearly MM(0) ¼ 1.
166
Characteristic Functions and Generating Functions
Mean: 0 MM ðtÞ ¼ pk qk(1 qet )k1 et 0 MM (0) ¼ E(M) ¼
kq p
and E(N) ¼
kq k þk ¼ p p
Variance. We first find EfM 2g ¼ m2: 00 MM (t) ¼ pk qk(1 qet )k2 (kqet þ 1)et 00 MM (0) ¼ E(M 2 ) ¼
qk(1 þ qk) p2
Hence the variance of M is var½M ¼
qk(1 þ qk) (qk)2 qk 2 ¼ 2 p2 p p
The variance of N can be calculated by using N ¼ M þ k in the equations above. The MGF of the Gaussian distribution is
Example 11.5.2 (Gaussian Distribution) given by 1 MX (t) ¼ pffiffiffiffiffiffi 2p s
ð1
e½(xm)
2
=2s2 þtx
dx
1
Completing the squares, we obtain 2 2
MX (t) ¼ etmþ½ðs t
Þ=2
ð1 1 (x m ts2 )2 pffiffiffiffiffiffi exp dx 2s2 2p s 1
The value of the integral within braces is 1, and hence the MGF of a Gaussian is 2 2
MX (t) ¼ etmþ½(s t
)=2
Mean: MX0 (t) ¼ etm½(s
2 2
t )=2
(m þ s2 t)
MX0 (0) ¼ m Variance: MX00 (t) ¼ etmþ½(s
2 2
t )=2
MX00 (0) ¼ m þ s2
2 2
(m þ s2 t)2 þ etmþ½(s t
and
var½X ¼ s2
)=2 2
s
11.6
Example 11.5.3 (Gamma Distribution) given by
167
We will find the MGF of a gamma density
l(lx)a1 elx G(a)
fX (x) ¼
Cumulant Generating Functions
a, l; x . 0
From Eq. (11.5.1), we have ð1
l(lx)a1 elx tx e dx, a, l . 0 G(a) 0 ð la 1 a1 (lt)x x e dx ¼ G(a) 0
MX (t) ¼
Substituting (l 2 t)x ¼ y and using the definition of a gamma function [Eq. (7.5.2)], we can write this equation as ð1 la MX (t) ¼ ya1 ey dy G(a)(l t)a 0 MX (t) ¼
la la a G(a) ¼ G(a)(l t) (l t)a
Mean: la (a)(1) ala ¼ (l t)aþ1 (l t)aþ1 a MX0 (0) ¼ l MX0 (t) ¼
Variance. We first find EfX 2g ¼ m2: MX00 (t) ¼
a(a þ 1)la (l t)aþ2
MX00 ð0Þ ¼ m2 ¼
a(a þ 1)la a2 þ a ¼ l2 laþ2
Hence the variance is m2 m2X ¼
a2 þ a a2 a 2¼ 2 l2 l l
The means and variances of most of the commonly occurring discrete (Chapter 4) and continuous (Chapters 6 and 7) distributions are shown in Section 11.7.
B 11.6
CUMULANT GENERATING FUNCTIONS
The cumulant generating function (CGF) KX(t) is defined as the logarithm of the moment generating function MX(t), or KX (t) ¼ ln½MX (t). The cumulants also called semiinvariants {ki } are the coefficients of CGF: KX (t) ¼ ln½MX (t) ¼ tk1 þ
1 i X t2 tn t k2 þ þ kn þ ¼ ki 2! n! i! i¼0
(11:6:1)
168
Characteristic Functions and Generating Functions
We will now find the cumulants in terms of the moments mk. From Eq. (11.5.2), we can expand ln[MX(t)] in terms of a Taylor series as follows: t 2 m2 t n mn þ þ þ KX (t) ¼ ln½MX (t) ¼ ln 1 þ tm1 þ 2! n! t2 m2 t n mn ¼ tm1 þ þ þ þ 2! n! 2 1 t 2 m2 tn mn tm1 þ þ þ þ 2 2! n! 3 1 t 2 m2 tn mn tm1 þ þ þ þ + þ 3 2! n!
(11:6:2)
Collecting terms in t, we have KX (t) ¼ tm1 þ
t2 (m2 m21 ) 2!
þ
t3 (m3 3m1 m2 þ 2m31 ) 3!
þ
t4 (m4 3m22 4m1 m3 þ 12m21 m2 6m41 ) 4!
þ
t5 (m5 5m4 þ 30m1 m22 þ 40m21 m3 60m31 m2 24m51 ) 5!
þ
(11:6:3)
Equating coefficients of ti in Eqs. (11.6.1) and (11.6.3), the cumulants have been expressed in terms of the moments fmig in Eq. (11.6.4), where they are also expressed in terms of the central moments vi ¼ E½X mX i : k1 ¼ m1 ¼ mX k2 ¼ m2 m21 ¼ s2X ¼ v2 k3 ¼ m3 3m1 m2 þ 2m31 ¼ v3
(11:6:4)
k4 ¼ m4 3m22 4m1 m3 þ 12m21 m2 6m41 ¼ v4 3v2 k5 ¼ m5 5m4 þ 30m1 m22 þ 40m21 m3 60m31 m2 24m51 ¼ v5 10v2 v3 þ Skewness measures the lack of symmetry of the tails of a density. A density is symmetric if it looks the same on both the left and the right of the centerpoint. Skewness is defined as v3 g1 ¼ 3=2 (11:6:5) v2 Kurtosis is defined as a normalized form of the fourth central moment of a distribution. It is a measure of whether the density is peaked or flat relative to a normal distribution. High-kurtosis density functions will have a high peak near the mean, and low-kurtosis
11.6
Cumulant Generating Functions
functions will have a flat top. Kurtosis is defined as v4 g2 ¼ 2 v2
169
(11:6:6)
For Gaussian distributions the kurtosis is 3. Example 11.6.1 We will find the cumulants of the Poisson distribution given by p(k; l) ¼ el (lk =k!). t The MGF of the Poisson distribution is MX (t) ¼ el(1e ) , and the CGF is given by t KX (t) ¼ ln el(1e ) ¼ l þ let Expanding KX (t) in Taylor series, we have t2 t3 t4 t5 l þ let ¼ l t þ þ þ þ þ 2! 3! 4! 5! Thus, all the cumulants are equal to l, or ki ¼ l, i ¼ 1,2, . . . : Example 11.6.2
We will find the cumulants of a Gaussian distribution " # 1 1 x mX 2 fX (x) ¼ pffiffiffiffiffiffi exp 2 sX 2psX 2 2
The MGF is given by MX (t) ¼ etmX þ½(t sX )=2 and the CGF, by h i t2 s2X 2 2 KX (t) ¼ ln etmX þ½(t sX )=2 ¼ tmX þ 2 and the cumulants are k1 ¼ mX , k2 ¼ s2X and ki ¼ 0 for i . 2. Thus, for the Gaussian distribution, there are only two cumulants. Joint Cumulants
By analogy to Eq. (11.6.4), we can define joint cumulants for one to four random variables, X1, X2, X3, X4, as follows: k1 ¼ E½X1 ¼ mX k12 ¼ E½X1 X2 E½X1 E½X2 ¼ sX1 X2 k123 ¼ E½X1 X2 X3 E½X1 E½X2 X3 E½X2 E½X1 X3 E½X3 E½X1 X2 þ 2E½X1 E½X2 E½X3 k1234 ¼ E½X1 X2 X3 X4 E½X1 X2 E½X3 X4 E½X1 X3 E½X2 X4 E½X1 X4 E½X2 X3 E½X1 E½X2 X3 X3 E½X2 E½X1 X3 X4 E½X3 E½X1 X2 X4 E½X4 E½X1 X2 X3 þ 3E½X1 E½X2 E½X3 X4 þ 3E½X2 E½X3 E½X1 X4 þ 3E½X3 E½X4 E½X1 X2 þ 3E½X1 E½X4 E½X2 X3 6E½X1 E½X2 E½X3 E½X4
(11:6:7)
170
Characteristic Functions and Generating Functions
If the mean values of random variables Z1, Z2, Z3, Z4 are zero, then Eq. (11.6.7) can be considerably simplified as follows: k1 ¼ E½Z1 ¼ 0 k12 ¼ E½Z1 Z2 k123 ¼ E½Z1 Z2 Z3 k1234 ¼ E½Z1 Z2 Z3 Z4 E½Z1 Z2 E½Z3 Z4
(11:6:8)
E½Z1 Z3 E½Z2 Z4 E½Z1 Z4 E½Z2 Z3 If X1, X2, Y1, Y2 are four random variables, not necessarily independent, we will express the cov[X1 X2, Y1 Y2] in terms of the joint cumulant k1234 : cov½X1 X2 , Y1 Y2 ¼ E{½X1 X2 E(X1 X2 )½Y1 Y2 E(Y1 Y2 )} ¼ E½X1 X2 Y1 Y2 E½X1 X2 E½Y1 Y2
(11:6:9)
Expressing E½X1 X2 Y1 Y2 in terms of the fourth cumulant k1234 given in Eq. (11.6.8), we can rewrite Eq. (11.6.9) as follows: cov½X1 X2 ; Y1 Y2 ¼ k1234 þ E½X1 Y1 E½X2 Y2 þ E½X1 Y2 E½X2 Y1
B 11.7
(11:6:10)
TABLE OF MEANS AND VARIANCES pþq¼1
Random Variable
Mean
Variance Discrete Distributions
Bernoulli
p
pq
Binomial
Np
Npq or Np(1 2 p)
Multinomial
npi , q p
Geometric
i ¼ 1, . . . , m
Negative binomial E(M) ¼ Hypergeometric
Kn N
Poisson
l
Logarithmic— Benford
kq p
npi (1 pi ), i ¼ 1, . . . , m q p2 kq for M ¼ N k var(M) ¼ 2 for M ¼ N k p nK(N K)(N n) N 2 (n 1) l
2 8 2 :5 log ffi 3:44 7 : 34 (d ¼ 1, . . . , 9)
2 8 2 28 : 572 2 :5 ffi 6:06 log 13 50 log 7 :3 7 : 34 (d ¼ 1, . . . , 9)
Continuous Distributions Uniform
bþa 2
(b a)2 12 (Continued )
11.7
Random Variable
Table of Means and Variances
Mean 1 l
Variance 1 l2
Gaussian
m
Triangular
b
s2 (b a)2 6 2 l2 n , n integer l2 a l2 1 aþ2 2 aþ1 G G a a l2
Exponential
Laplace—double- 0 exponential n , n integer Erlang l a Gamma l 1 aþ1 Weibull mþ G l a Chi-square
Chi
Rayleigh Maxwell
ns2
2ns4
pffiffiffi nþ1 2sG 2 n G 2 rffiffiffiffi p s 2 rffiffiffiffi 2 2s p
3 nþ1 2G 6 7 2 n 7 n s2 6 4 5 G2 2 p s2 2 2 8 2 s 3 p
mR ¼ s2
Rice
2
2
171
rffiffiffiffi 2 2 p (m2 =4s2 ) m2 m m2 m 1 þ 2 I0 I þ e 1 2 2s 4s2 2s2 4s2
s2R ¼ m2 þ 2s2 m2R ; m: noncentrality parameter
Nakagami
1 1=2 G m þ V 2 m G(m)
3 2 1 2 G mþ V6 2 7 7 V 6 4 m G(m) 5 n , n.2 v2
Student-t
0
Snedecor F
n2 , n2 2
Lognormal
e½mþ(s =2)
2v22 (n1 þ n2 2) , n1 (n2 2)2 (n2 4) 2 2 e(2mþs ) es 1
Beta
a aþb
ab (a þ b þ 1)(a þ b)2
2
n2 . 2
v2 . 4
(Continued )
172
Characteristic Functions and Generating Functions
Random Variable
Mean
Variance
Cauchy
Not defined
Not defined
Pareto
b , a1
ab2 , a.2 (a 1)2 (a 2)
a.1
CHAPTER
12
Functions of a Single Random Variable
B 12.1
RANDOM VARIABLE g(X )
In many probability problems, we have to find the pdf of a function of random variables. We have already defined (in Chapter 5) a random variable X as the mapping from the sample space S to space of real numbers Rx; that is, it is the set of all outcomes j belonging to the sample space S such that the unique number X(j) belongs to the real line Rx. Thus, the random variable X is a mapping of the probability space {S, F, P} to the probability space on the real line {Rx , FX , PX }: X: {S, F, P} ! {Rx , FX , PX }
(12:1:1)
We can now define a random variable Y as a function of another random variable X, or Y ¼ g(X ), as the mapping from the real line Rx to another real line Ry, such that it induces a probability space {Ry , FY , PY }. In other words, the random variable Y is a composite function of the random variable X such that Y(j) ¼ g(X ) ¼ g[X (j)]. The mapping is given by Y: {S, F, P} ! {Rx , FX , PX } ! {Ry , FY , PY }
(12:1:2)
and shown in Fig. 12.1.1. Conditions on Y 5 g(X ) to be a Random Variable 1. The domain of g(x) must include the range of the random variable (RV) X. 2. For each y [ Iy, fg(x) yg must form a field FY ; that is, it must consist of the unions and intersections of a countable number of intervals of the real line Ry. 3. The probability of the event fg(x) ¼ +1g is 0. In essence, the transformation Y ¼ g(X ) induces a probability measure PY on FY , and we have to find this probability measure. From Fig. 12.1.1 we can write PY {Y} ¼ PX {g1 ( Y)} ¼ PX {x : g1 ( Y) , Ix }
(12:1:3)
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
173
174
Functions of a Single Random Variable
FIGURE 12.1.1
The set Ix consists of all the countable unions and intersections of the type a , x b. Without loss in generality, we will assume that Ix ¼ {X y}. Thus, Eq. (12.1.3) becomes PY {Y} ¼ P{Y y} ¼ FY ( y) ¼ PX {x : g1 ( Y) y} ¼ PX {x : g(x) y}
(12:1:4)
In conclusion, we can say that the probability of Y y is given by the probability of the set of all values of x such that g(x) y.
B 12.2
DISTRIBUTION OF Y 5 g(X )
We will find the distribution FY (y) of the RV Y ¼ g(X) in terms of the distribution FX (x) of the RV X and the function g(x). We shall do this with an arbitrary function g(x) bounded between a and b as shown in Fig. 12.2.1. As y traverses from 21 to þ1 along the g(x) axis, we have to find the values of x along the x axis that satisfy g(x) y. In Fig. 12.2.1 there are five distinct regions for y corresponding to the intersections of y with g(x): 1. y a, no points of intersection 2. a , y y1, one point of intersection x1 3. y1 , y y2, three points of intersection x2, x20 , x002 4. y2 , y b, one point of intersection x3 5. y . b, no points of intersection
FIGURE 12.2.1
12.2 Distribution of Y 5 g(X )
175
We have to find FY ( y) ¼ probability of g(x) y in each of these five regions. 1. y a: There are no points along the x axis for which g(x) y, or IX ¼ {1}; hence FY ( y) ¼ 0. 2. a , y y1: The set of all points along the x axis for which g(x) y, or Ix ¼ (x1, 1); hence FY ( y) ¼ 1 2 FX(x1). 3. y1 , y y2: The set of all points along the x axis for which g(x) y, or Ix ¼ (x2, x20 ] < (x002, 1); hence FY ( y) ¼ FX (x20 ) 2 FX (x2) þ 1 2 FX (x002). 4. y2 , y b: The set of all points along the x axis for which g(x) y or Ix ¼ (x3, 1); hence FY ( y) ¼ 1 2 FX(x3). 5. y . b: Here the entire g(x) is below y; and hence Ix ¼ (21, 1); hence FY ( y) ¼ 1. Thus, we have used Eq. (12.1.4) to find PfY y) along the entire g(x) axis. Special Case
We now consider a situation where g(x) ¼ y1 for x1 , x x2. In this case we have P{Y ¼ y1 } ¼ P{x1 , X , x2 } ¼ FX (x2 ) FX (x1 )
(12:2:1)
Hence FY ( y) is discontinuous at y ¼ y1, and the discontinuity is equal to FX (x2 ) FX (x1 ). Summary of Steps for Finding FY( y) 5 PfY y) 1. Graph the function g(x) along the x and g(x) axes. Do not label the g(x) axis as y. 2. Draw y parallel to the x axis. 3. Find the distinct regions along the g(x) axis. The regions are determined by the number of intersections of y with g(x) as y goes from 21 to 1 along the g(x) axis. Let these be y1, y2, . . . . 4. In each region solve for all values of x for which y ¼ g(x). Let these be x, x0 , x00 , . . . . 5. For each of these regions, find Ix such that g(x) y. 6. Find FY( y) ¼ PfY yg ¼ Pfx [ Ix) ¼ Pfx : g(x) yg. We will take a number of examples of increasingly difficult transformations g(x) and solve for PfY yg using the steps listed above. We shall assume that in all the following examples FX(x) or fX(x) is given and we have to find FY( y). Example 12.2.1 Given that Y ¼ aX þ b with a . 0, we have to find FY( y). We follow the steps listed above: 1. The function g(x) ¼ ax þ b has been graphed. 2. y parallel to the x axis has been drawn in Fig. 12.2.2. 3. In this simple example there is only one region 21 , y , 1. 4. In this region, solving for y ¼ ax þ b, we have x ¼ ( y 2 b)/a. 5. The region Ix such that g(x) y is given by yb Ix ¼ 1 , x a y b yb 6. FY ( y) ¼ P{x : g(x) y} ¼ P X ¼ FX a a
176
Functions of a Single Random Variable
FIGURE 12.2.2
We can now find the density fY ( y) by differentiating FY ( y) with respect to y. The result is fY ( y) ¼
Example 12.2.2 before.
dFX (ð y bÞ=a) 1 yb ¼ fX dy a a
We repeat Example 12.2.1 with a 0. We follow the same steps as
1. The function g(x) is graphed in Fig. 12.2.3. 2. y has been drawn parallel to x. 3. In this example also, there is only one region 21 , y ,1. 4. In this region, solving for y ¼ ax þ b, we have x ¼ ( y 2 b)/a or x ¼ [( y 2 b)/jaj]. 5. The region IX such that g(x) y is given by
6.
yb ,x1 IX ¼ jaj yb FY ( y) ¼ P{x : g(x) y} ¼ P X . jaj yb yb ¼ 1 FX ¼1P X . jaj jaj
The corresponding density function fY ( y) is given by differentiating FY ( y) with respect to y, and the result is dFX fY ( y) ¼
yb 1 yb a fX ¼ jaj a dy
The result is very similar to that obtained in Example 12.2.1 except for the absolute value of a.
12.2 Distribution of Y 5 g(X )
177
FIGURE 12.2.3
Example 12.2.3 Let g(x) ¼ (x 2 a)2. This example is different from the previous two examples because there are multiple regions for y. Following similar steps, we obtain 1. The function g(x) is graphed in Fig. 12.2.4 and 2. y has been drawn parallel to x. 3. Here the two regions of y are fy 0g, no points of intersection and fy . 0g, two points of intersection. 4. In the region fy 0g, Ix ¼ 1. In the region fy.0g, we solve for y ¼ g(x) and pffiffiffi pffiffiffi obtain x1 ¼ a þ y and x2 ¼ a y. pffiffiffi pffiffiffi 5. Thus, Ix ¼ (a y, a þ y , y . 0. 6. In the first region FY ( y) ¼ 0 : y 0. In the second region pffiffiffi pffiffiffi y , X a þ y) pffiffiffi pffiffiffi ¼ FX (a þ y ) FX (a y ) : y . 0
FY ( y) ¼ P(a
FIGURE 12.2.4
178
Functions of a Single Random Variable
The corresponding density function is fY ( y) ¼ 0 : y 0 fY ( y) ¼
d pffiffiffi pffiffiffi (FX (a þ y ) FX (a y )) dy
1 pffiffiffi pffiffiffi ¼ pffiffiffi (fX (a þ y ) þ fX (a y )) : y . 0 2 y If X is a zero mean Gaussian distributed random variable given by 1 2 2 fX (x) ¼ pffiffiffiffiffiffi e(x =2s ) s 2p and if Y ¼ X 2, then fY ( y) is given by 1 1 1 2 2 2 fY ( y) ¼ pffiffiffi pffiffiffiffiffiffi e( y=2s ) þ e( y=2s ) ¼ pffiffiffiffiffiffiffiffi e( y=2s ) 2 y s 2p s 2py which is a chi-square distribution with one degree of freedom [Eq. (7.7.1)].
Example 12.2.4 This example is a complement of Example 12.2.3. Here g(x) ¼ 1=(x a)2 and we have to find FY ( y). We follow the same procedure laid out earlier. 1. The function g(x) is graphed in Fig. 12.2.5. 2. y has been drawn parallel to the x axis. 3. Here the two regions of y are fy 0g, no points of intersection and fy . 0g, two points of intersection. 4. In the region f y 0g, Ix ¼ {1} and FY( y) ¼ 0. In the region f y . 0g, we solve for pffiffiffi pffiffiffi y ¼ g(x) and obtain x1 ¼ a þ ð1= y Þ and x2 ¼ a (1= y ). 5. Thus 1 1 Ix ¼ 1, a pffiffiffi < a þ pffiffiffi , 1 y y
FIGURE 12.2.5
12.2 Distribution of Y 5 g(X )
179
6. In the region indicated in item 5 (above), we obtain 1 1 FY ( y) ¼ P X a pffiffiffi þ 1 P X a þ pffiffiffi y y 1 1 ¼ FX a pffiffiffi þ 1 FX a þ pffiffiffiffi : y . 0 y Y The corresponding density function is fY ( y) ¼ 0 : y 0 d 1 1 FX a pffiffiffi þ 1 FX a þ pffiffiffi fY ( y) ¼ dy y y 1 1 1 ¼ 3=2 fX a pffiffiffi þ fX a þ pffiffiffi : y . 0 2y y y Example 12.2.5 The previous examples illustrated continuous functions of g(x). In this example g(x) is a discrete function as shown in Fig. 12.2.6. Note that g(x) is not a true function because there are many points of y at x ¼ +a. We can still find FY ( y) given FX (x). The procedure is no different from that in the previous examples. 1. The function g(x) has already been graphed in Fig. 12.2.6. 2. y has been drawn parallel to the x axis. 3. There are four region of y, given by (1) y 2b, (2) 2b , y 0, (3) 0 , y b, (4) y . b. 4. The intersection points are (1) none, (2) x1 ¼ 2a, (3) x2 ¼ a, (4) none. 5. The corresponding four regions along the x axis are (1) Ix ¼ {1}, (2) Ix ¼ {1, a}, (3) Ix ¼ f1, a}, (4) Ix ¼ {1, 1}. 6. The distribution function FY( y) in the various regions are (1) y 2 b : FY ( y) ¼ 0, (2) 2b , y 0 : FY ( y) ¼ FX (2a), (3) 0 , y b : FY ( y) ¼ FX (a), (4) y . b : FY ( y) ¼ 1. The distribution function FY( y) is a “staircase” function, indicating that Y is a discrete random variable and the density function fY ( y)
FIGURE 12.2.6
180
Functions of a Single Random Variable
FIGURE 12.2.7
is (a) fY( y) ¼ 0, (b) fY( y) ¼ FX (2a)d(x þ b), (c) fY( y) ¼ [FX(a) 2 FX(2a)]d(x), (d) [1 2 FX(a)]d(x 2 b). The distribution and density functions are shown in Fig. 12.2.7.
Example 12.2.6 We shall now take an example of a piecewise-continuous g(x) with a specified fX(x) and FX(x). The functions g(x), fX(x) and FX(x) are shown in Fig. 12.2.8.
FIGURE 12.2.8
12.2 Distribution of Y 5 g(X )
181
We have to find FY( y) given that fX(x) is defined by Fig. 12.2.8 as 2
3 1 : 3 , x 2 5 fX (x) ¼ 4 5 0: otherwise Since fX(x) is specified, FX(x) is determined by integrating fX(x). The result is 2
0: 6x 3 FX (x) ¼ 4 þ : 5 5 1:
x 3
3
7 3 , x 2 5 x.2
and is shown in Fig. 12.2.8. In this example, the range of fX(x), x [ f23, 2g has to be factored in determining FY ( y). Other than that, we will still follow the procedure laid out earlier. 1. The function g(x) has already been graphed in Fig. 12.2.8. 2. y will scan from 21 to 1 along the g(x) axis. 3. The regions of y are given by (a) y 24, (b) 24 , y 22, (c) 22 , y21, (d) 21 , y 0, (e) 0 , y 1, (f) 1 , y 2, (g) 2 , y 4, (h) y . 4. 4. The points of intersection of y with g(x) are (a) none, (b) x ¼ 22, (c) x ¼ y, (d) x ¼ 0, (5) x ¼ 1, (6) x ¼ y, (7) x ¼ 2, (8) none. 5. The corresponding regions Ix are (a) Ix ¼ f1g, (b) Ix ¼ f21, 22g, (c) Ix ¼ f21, yg, (d) Ix ¼ f21, 0g, (e) Ix ¼ f21, 1g, (f) Ix ¼ f21, yg, (g) Ix ¼ f21, 2g, (h) Ix ¼ f21, 1g. 6. The distributions functions in the six regions are (a) y 4 : FY ( y) ¼ 0 1 (b) 4 , y 2 : FY ( y) ¼ FX (2) FX (4) ¼ 5 y 3 (c) 2 , y 1 : FY ( y) ¼ FX ( y) ¼ þ 5 5 3 (d) 1 , y 0 : FY ( y) ¼ FX (0) ¼ 5 4 (e) 0 , y 1 : FY ( y) ¼ FX (1) ¼ 5 y 3 (f) 1 , y 2 : FY ( y) ¼ FX ( y) ¼ þ 5 5 (g) 2 , y . 4 : FY ( y) ¼ FX (2) ¼ 1 since FX (x) ¼ 1 for all values of x . 2 (h) y . 4 : FY ( y) ¼ 1 By differentiating FY( y) in every region, we can express the density function fY ( y) as 1 1 1 1 fY ( y) ¼ d( y þ 3) þ ½u( y þ 1) u( y þ 2) þ d( y þ 1) þ d( y) 5 5 5 5 1 þ ½u( y 2) u( y 1) 5 where u( y) is the usual step function. FY ( y) and fY ( y) are graphed in Fig. 12.2.9.
182
Functions of a Single Random Variable
FIGURE 12.2.9
Example 12.2.7 This example is similar to the previous one with a piecewisecontinuous function g(x). The functions g(x), fX(x) and FX(x) are given by 2
3 0: x 2 "1 # 6 1: 2 , x 0 7 6 7 : 2 , x 2 7 ; g(x) ¼ 6 6 2 x: 0 , x 3 7; fX (x) ¼ 4 4 5 0: otherwise 3 2: x.3 2 3 0: x 2 6x 1 7 FX (x) ¼ 4 þ : 2 , x 2 5 4 2 1: x,2
The functions g(x), fX(x), and FX(x) are shown in Fig. 12.2.10. We have to find FY( y). Following the same procedure as before 1. The function g(x) has been graphed in Fig. 12.2.10. 2. y has been drawn, and the intersection of y with g(x) is given by x ¼ 32y. 3. The regions of y are given by (a) y 21, (b) 21 , y 0, (c) 0 , y 43, (d) 43 , y 2, (e) y . 2. Even though g(x) extends upto x ¼ 3, the function fX(x) terminates at x ¼ 2 and FX(x) ¼ 1 beyond x ¼ 2. 4. The points of intersection of y with g(x) are (a) none, (b) x ¼ 22,0, (c) x ¼ 32y, (d) x ¼ 32y, (e) none. 5. The corresponding regions Ix are (a) Ix ¼ f1g, (b) Ix ¼ (– 2, 0], (c) Ix ¼ (21, 23 x], (d) Ix ¼ (21, 23x], (e) Ix ¼ (21, 1]. 6. The distribution functions in the four regions are (a) y 21: FY ( y) ¼ 0, (b) 21 , y 0: FY( y) ¼ FX(0) 2 FX(22) ¼ 12, (c) 0 , y 43: FY( y) ¼ FXð32 yÞ ¼ 3 1 4 8 y þ 2, (d) 3 , y 2: FY ( y) ¼ 1, (e) y 2: FY( y) ¼ 1.
12.2 Distribution of Y 5 g(X )
FIGURE 12.2.10
The corresponding density function fY ( y) is given by 1 3 4 fY ( y) ¼ d( y þ 1) þ u y u( y) 2 8 3 The functions FY ( y) and fY ( y) are shown in Fig. 12.2.11.
Example 12.2.8 We shall now take a more complicated example and find FY ( y). In this example g(x) is defined by g(x) ¼
1 x2 1
for all x
FIGURE 12.2.11
183
184
Functions of a Single Random Variable
FIGURE 12.2.12
The probability density function fX(x) and the distribution function FX(x) are given by 2
2 fX (x) ¼ 4 9 0
3 if 1:5 , x 3 5 ; otherwise
2
0 1 62 FX (x) ¼ 4 x þ 9 3 1
3
if
x 1:5
if
7 1:5 x 3 5
if
x.3
The function g(x) is shown in Fig. 12.2.12 and fX(x) and FX(x) are shown in Fig. 12.2.13. 1. g(x) has been graphed in Fig. 12.2.12. 2. y scans from 21 to þ1 along the g(x) axis. 3. The regions of y are given by (a) y 21, two points of intersection in the range of fX(x), (21.5, 3]; (b) 21 , y 0, no points of intersection; (c) 0 , y 18, two points of intersection beyond the range of fX(x), (21.5, 3]; (d) 18 , y 45, one point of intersection in the range of fX(x), (21.5, 3], (e) y . 45, two points of intersection in the same range. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4. The p points of ffiintersection of y with g(x) areffi (a) x1 ¼pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 þ (1=y) and ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi x2 ¼ 1 þ (1=y), (b) none, (c) x1 ¼ 1 þ (1=y) and x2 p ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 þ (1=y) beyond ffi the range of (21.5, 3], (d) one point of intersection x2 ¼ 1 þ (1=y) within the
FIGURE 12.2.13
12.2 Distribution of Y 5 g(X )
185
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rangep and anotherffi x1 ¼ 1 þ (1=y) beyond the range, (e) x1 ¼ 1 þ (1=y) and ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x2 ¼ 1 þ (1=y). 5. The corresponding regions Ix are sffiffiffiffiffiffiffiffiffiffiffi) (sffiffiffiffiffiffiffiffiffiffiffi ) ( 1 1 ðaÞ Ix ¼ 1, 1 þ < 1þ , 1 y y ðbÞ Ix ¼ (1, 1 sffiffiffiffiffiffiffiffiffiffiffi) (sffiffiffiffiffiffiffiffiffiffiffi ) 1 1 Ix ¼ 1, 1 þ < {1, 1} 1þ , 1 y y (
ðcÞ
sffiffiffiffiffiffiffiffiffiffiffi) (sffiffiffiffiffiffiffiffiffiffiffi ) 1 1 ðdÞ Ix ¼ 1, 1 þ < {1, 1} 1þ , 1 y y (
sffiffiffiffiffiffiffiffiffiffiffi) (sffiffiffiffiffiffiffiffiffiffiffi ) 1 1 Ix ¼ 1, 1 þ < {1, 1} 1þ , 1 y y (
ðeÞ
6. The distribution functions in the various regions are sffiffiffiffiffiffiffiffiffiffiffi! sffiffiffiffiffiffiffiffiffiffiffi! 1 1 1þ ðaÞ y 1 : FY ( y) ¼ FX 1 þ FX (1) þ FX (1) FX y y sffiffiffiffiffiffiffiffiffiffiffi 2 1 1 2 1 2 1 þ 1þ þ þ ¼ þ 9 y 3 9 3 9 3 sffiffiffiffiffiffiffiffiffiffiffi ! 2 1 1 1þ þ 9 y 3 sffiffiffiffiffiffiffiffiffiffiffi 4 4 1 1þ ¼ 9 9 y
ðbÞ
ðcÞ
1 , y 0 : FY ( y) ¼ FX (1) þ FX (1) ¼
2 1 2 1 4 þ þ ¼ 9 3 9 3 9
sffiffiffiffiffiffiffiffiffiffiffi! 1 1 0 , y : FY ( y) ¼ FX 1 þ FX (1) þ FX (1) FX (1) 8 y sffiffiffiffiffiffiffiffiffiffiffi! 1 þ FX (1) FX 1þ y 4 4 ¼00þ þ11¼ 9 9
186
Functions of a Single Random Variable
sffiffiffiffiffiffiffiffiffiffiffi! 1 4 1 , y : FY ( y) ¼ FX 1 þ ðdÞ FX (1) þ FX (1) FX (1) 8 5 y sffiffiffiffiffiffiffiffiffiffiffi! 1 þ FX (1) FX 1þ y sffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffi ! 4 2 1 1 10 2 1 1þ þ 1þ ¼00þ þ1 ¼ 9 9 y 3 9 9 y sffiffiffiffiffiffiffiffiffiffiffi! 4 1 ðeÞ y . : FY ( y) ¼ FX 1 þ FX (1) þ FX (1) FX (1) 5 y sffiffiffiffiffiffiffiffiffiffiffi! 1 þ FX (1) FX 1þ y sffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffi ! ! 2 1 1 4 2 1 1 1þ þ 1þ þ ¼ 0þ þ1 9 y 3 9 9 y 3 sffiffiffiffiffiffiffiffiffiffiffi 13 4 1 ¼ 1þ 9 9 y The density function fY ( y) can be obtained by differentiating FY ( y). Functions FY ( y) and fY ( y) are shown in the following equation. FY ( y) is graphed in Fig. 12.2.14a, and fY ( y) is graphed in Fig. 12.2.14b. sffiffiffiffiffiffiffiffiffiffiffi 3 2 4 4 1 1þ if y 1 7 6 7 6 9 9 y 7 6 64 17 6 if 1 , y 7 69 87 7 6 sffiffiffiffiffiffiffiffiffiffiffi 7; FY ( y) ¼ 6 6 10 2 1 1 4 7 7 6 6 9 9 1 þ y if 8 , y 5 7 7 6 7 6 sffiffiffiffiffiffiffiffiffiffiffi 7 6 5 4 13 4 1 4 1þ if y. 9 9 y 5 3 22 1 rffiffiffiffiffiffiffiffiffiffiffi if y 1 7 69 2 7 6 y 1þ1 7 6 y 7 6 6 17 60 if 1 , y 7 6 87 7 6 61 1 1 4 7 fY ( y) ¼ 6 7 6 9 rffiffiffiffiffiffiffiffiffiffiffi1 if 8 , y 5 7 7 6 2 7 6 y 1þ 7 6 y 7 6 62 1 4 7 7 6 y. 4 9 rffiffiffiffiffiffiffiffiffiffiffi1 if 5 5 2 y 1þ y
12.2 Distribution of Y 5 g(X )
187
FIGURE 12.2.14
Example 12.2.9 In Example 12.2.8 we will find the conditional probability of Y conditioned on the event A ¼ jXj . 1. Since jXj . 1, the part of the function g(x) that lies between 21 and 1 has no effect on the conditional distribution. Thus, g(x) is given by
g(x) ¼
1 x2 1
for
jxj , 1
The conditional density function fX(x j A) with A ¼ jXj . 1 is given by
fX (x j A) ¼
fX (x) fX (x) ¼ p(jXj . 1) FX (1) þ 1 FX (1) f (x) 9 X ¼ fX (x) ¼ 2 1 2 1 5 þ þ þ1 9 3 9 3
for
jxj . 1
188
Functions of a Single Random Variable
Hence, the conditional density fX(x j A) and the conditional distribution FX(x j A) can be written as 22 3 if 1:5 , x 1 65 7 6 7 fX (x j A) ¼ 6 2 7; if 1,x3 5 4 5 0 otherwise 3 2 0 if x , 1:5 7 6 2x þ 3 if 1:5 , x 1 7 6 7 6 5 7 6 1 7 6 if 1 , x 1 7 FX (x j A) ¼ 6 7 6 5 7 6 2x 1 6 if 1,x3 7 5 4 5 1 if x.3 The function g(x j A) is shown in Fig. 12.2.15. f (x j A) and FX(x j A) are shown in Fig. 12.2.16. Using exactly the same procedure as in Example 12.3.9, we can find the conditional distribution FY ( y j A) and the conditional density fY ( y j A) for the regions (a) y 18, (b) 18 , y 45, and (c) y . 45. The results are shown below: 3 2 1 if y 7 60 8 7 6 sffiffiffiffiffiffiffiffiffiffiffi! 7 6 1 1 47 6 2 7 6 3 1 þ , x if FY ( y j A) ¼ 6 5 y 8 57 7 6 sffiffiffiffiffiffiffiffiffiffiffi! 7 6 7 61 1 4 5 4 94 1þ if y . 5 y 5 2
0
6 1 6 0 6 6 C 61B 1 6 B rffiffiffiffiffiffiffiffiffiffiffiC 65@ 1A 6 y2 1 þ fY ( y j A) ¼ 6 y 6 0 1 6 6 6 B C 1 62B 6 @ rffiffiffiffiffiffiffiffiffiffiffiC 45 1A y2 1 þ y
if
if
if
y
1 8
3
7 7 7 7 1 47 ,x 7 8 57 7 7 7 7 7 7 4 7 7 y. 5 5
FY ( y j A) and fY (Y j A) are also graphed in Fig. 12.2.17.
Example 12.2.10 In the previous examples g(x) was explicitly given, and we had to find FY ( y) given fX(x). In this example g(x) is not explicitly given, but we will have to determine it from the problem. Points are uniformly distributed along the circumference of a unit circle. From each point a tangent is drawn that intersects the x axis (Fig. 12.2.18). We have to find the distribution of the points of intersection along the x axis. Since the
12.2 Distribution of Y 5 g(X )
189
FIGURE 12.2.15
points are uniformly distributed, the angle Q that the point subtends at the center of the circle is a random variable that is uniformly distributed as U(0,2p). The transformation mapping X ¼ g(Q) between Q and X is found as follows. Since the radius of the circle is 1, the cosine of the angle u subtended by any point on the unit circle at the center of the circle is given by cos(u) ¼ (1/x). Hence, the transformation between the random variable Q and the random variable X is given by X ¼ g(Q) ¼ sec(Q) The function g(u) is graphed in Fig. 12.2.19. The density function fQ(u) and the distribution function FQ(u) of the random variable Q are given by 2 3 0 if x 0 " 1 # for 0 , u 2p 6 u 7 ; FQ (u) ¼ 4 fQ (u) ¼ 2p if 0 , u 2p 5 2p 0 otherwise 1 if x . 2p They are shown in Fig. 12.2.20.
FIGURE 12.2.16
190
Functions of a Single Random Variable
FIGURE 12.2.17
The usual procedure can now be followed to determine the distribution function FX(x). 1. The function g(u) has been graphed. 2. The line x has been drawn in Fig. 12.2.19.
FIGURE 12.2.18
12.2 Distribution of Y 5 g(X )
191
FIGURE 12.2.19
3. The regions along the x axis are given by (a) x21, two points of intersection; (b) 21 , x 1, no points of intersection; (c) x . 1, two points of intersection. 4. Intersection points of x with g(u) are (a) u1 ¼ cos21 (1/x) and u10 ¼ 2p 2 cos21(1/x), (b) no points of intersection, (c) u2 ¼ cos21(1/x) and u20 ¼ 2p 2 cos21(1/x). 5. The corresponding regions Iu are p 1 S 1 3p , cos1 (a) Iu ¼ 2p cos1 , 2 x x 2 p 3p , (b) Iu ¼ 2 2 1 S p 3p S 1 , (c) Iu ¼ 0, cos1 2p cos1 , 2p x 2 2 x 6. The distribution functions in the various regions are 1 1 1 3 1 1 1 1 1 cos1 þ 1 cos1 (a) FX (x) ¼ ¼ cos1 2p x 4 4 2p x x 2 p 3 1 1 (b) FX (x) ¼ ¼ 4 4 2
FIGURE 12.2.20
192
Functions of a Single Random Variable
FIGURE 12.2.21
(c) FX (x) ¼
1 1 1 1 1 1 1 1 cos1 þ þ 1 1 cos1 ¼ cos1 þ 2p x 2 2p x x 2 p
This distribution is called the arcsine law. The density function fX(x) is obtained by differentiating the distribution function FX(x). They are both shown below, and the graphs are shown in Fig. 12.2.21: 2
3 1 1 1 cos1 if x 1 6p 7 x 2 6 7 61 7 FX (x) ¼ 6 if 1 , x 1 7; 62 7 41 5 1 1 1 cos þ if x.1 p x 2 2 3 1 p ffiffiffiffiffiffiffiffiffiffiffiffi ffi if x 1 6 px x2 1 7 6 7 6 fX (x) ¼ 6 0 if 1 , x 1 7 7 4 5 1 pffiffiffiffiffiffiffiffiffiffiffiffiffi if x.1 px x2 1 Example 12.2.11 As a final example, we will take the case when g(u) ¼ cos(u) in the region 2p , u p. In the previous example g(x) ¼ sec(u), and here g(x) ¼ cos(u). We will also assume as in the previous example that fQ(u) is uniformly distributed in
193
12.2 Distribution of Y 5 g(X )
FIGURE 12.2.22
(2p, p]. Thus, fQ(u) and FQ(u) are given by " 1 fQ (u) ¼ 2p 0
for
p , u p otherwise
2
# ;
0 1 6 u FQ (u) ¼ 4 þ 2p 2 1
if if if
x0
3
7 p , u p 5 x.p
The function g(u) ¼ cos(u) is shown in Fig. 12.2.22. The density and distribution functions are graphed in Fig. 12.2.23. Following the usual procedure 1. g(u) has been graphed. 2. The line x has been drawn (Fig. 12.2.22). 3. The regions along the x axis are given by (a) x 21, no points of intersection; (b) 21 , x 1, two points of intersection; (c) x . 1, no points of intersection. 4. Intersection points of x with g(u) are (a) no points of intersection, (b) u1 ¼ 2cos21(x) and u10 ¼ cos21(x), (c) no points of intersection. 5. The corresponding regions Iu are (a) Iu ¼ {1}, (b) Iu ¼ { p, cos1 (x)} {cos1 (x), p}, (c) Iu ¼ f.
FIGURE 12.2.23
194
Functions of a Single Random Variable
FIGURE 12.2.24
6. The distribution functions in the various regions are (a) FX (x) ¼ 0 1 1 p 1 (b) 1 cos (x) þ þ FX (x) ¼ 2p 2 2p 2 p 1 1 1 1 1 þ cos (x) þ þ ¼ 1 cos1 (x) 2p 2 2p 2 p (c) FX (x) ¼ 1 Here also the distribution is the arcsine law. The density function fX(x) is obtained by differentiating the distribution function FX(x). The distribution and density functions are given below and graphed in Fig. 12.2.24. 2
0
if
1 6 FX (x) ¼ 4 1 cos (x) if p 1 if 2 0 if 6 1 ffi if fX (x) ¼ 6 4 ppffiffiffiffiffiffiffiffiffiffiffiffi 1 x2 0 if
x 1
3
7 1 , x 1 5; x.1 x 1
3
7 1 , x 1 7 5 x.1
B 12.3 DIRECT DETERMINATION OF DENSITY fY ( y) FROM fX (x) In the previous section, for any given Y ¼ g(X ), we determined fY ( y) from fX (x) through a circuitous route by converting fX (x) to FX(x), using the transformation g(X ) to find FY ( y), and then differentiating FY ( y) to obtain fY ( y) as shown in the schematic representation in Fig. 12.3.1. Consider the function g(x) given in Fig. 12.3.2. The probability of the random variable Y lying between y and y þ dy is given by fY ( y)dy ¼ P{y , Y y þ dy}
(12:3:1)
12.3 Direct Determination of Density fY ( y) from fX (x)
195
FIGURE 12.3.1
In Fig. 12.3.2 y ¼ g(x) has three real solutions: x1, x2, x3. Hence the set of values for which {y , g(x) y þ dy} is given by the union of the set of values corresponding to the three points of intersection, {x1 , X x1 þ dx1 } {x2 þ dx2 , X x2 } {x3 , X x3 þ dx3 }, where dx1 . 0, dx3 . 0, and dx2 , 0. By the conservation of probability measure, we have fY ( y)dy ¼ P{y , Y y þ dy} ¼ P{x1 , X x1 þ dx1 } þ P{x2 þ dx2 , X x2 } þ P{x3 , X x3 þ dx3 } ¼ fX (x1 )dx1 þ fX (x2 ) j dx2 j þ fX (x3 )dx3
(12:3:2)
We can now write fY ( y) ¼ fX (x1 )
dx1 jdx2 j dx3 þ fX (x3 ) þ fX (x2 ) dy dy dy
(12:3:3)
jdx2 j 1 ¼ 0 , dy jg (x2 )j
(12:3:4)
But dx1 1 ¼ 0 , jg (x1 )j dy
FIGURE 12.3.2
dx3 1 ¼ 0 jg (x3 )j dy
196
Functions of a Single Random Variable
Substituting Eq. (12.3.4) into Eq. (12.3.3), we obtain the result for fY ( y) directly from fX(x): fY ( y) ¼
fX (x1 ) fX (x2 ) fX (x3 ) þ 0 þ 0 0 jg (x1 )j jg (x2 )j jg (x3 )j
(12:3:5)
Equation (12.3.5) can be generalized to the case when there are n real solutions to the equation y ¼ g(x), given by {x1 , x2 , . . . , xn }: fY ( y) ¼
n X fX (xi ) 0 (x )j jg i i¼1
(12:3:6)
From the derivation it is clear that certain restrictions have to be satisfied if we have to determine fY ( y) directly from fX(X ). Conditions for Direct Determination of fX(X) 1. The derivative of g(x) must exist. 2. There can be no straight-line solutions to g(x) ¼ y. We now formulate the following steps for the direct determination of fY ( y) from fX(x): Steps for Direct Determination of fY ( y) 1. Check whether g(x) satisfies restrictions 1 and 2. 2. Solve for y ¼ g(x). Let the n real roots be fx1, x2, . . . , xng. 3. Determine
dg(x)
i ¼ 1, 2, . . . , n
dx , x¼xi 4. Calculate fY ( y) ¼
n X fX (xi ) jg0 (xi )j i¼1
Thus, if the restrictions on g(x) are satisfied, it is easier to calculate fY ( y) directly from fX(x). We will take some of the examples in the previous section and solve for fX(x) directly. Example 12.3.1
We will find fY ( y) for Y ¼ aX þ b with a . 0 or, a 0.
1. g(x) satisfies the restrictions. 2. We solve for y ¼ ax þ b. The solution yields x ¼ ( y 2 b)/a.
dg(x) d(ax þ b)
¼ jaj
¼ 3.
dx dx
4. fY ( y) ¼ 1 fX y b jaj a Example 12.3.2
(12:3:7)
Y ¼ g(X ) ¼ (X 2 a)2:
1. g(x) satisfies the restrictions. pffiffiffi pffiffiffi 2. Solving for x in y ¼ (x 2 a)2, we obtain two roots: x1 ¼ a y, x2 ¼ a þ y
12.3 Direct Determination of Density fY ( y) from fX (x)
3.
dg(x) d(x a)2
pffiffiffi
¼
dx dx ¼ j2(x a)j ¼ 2 y
4.
1 pffiffiffi pffiffiffi fY ( y) ¼ pffiffiffi ½ fX (a y ) þ fX (a þ y ); 2 y
Example 12.3.3
y.0
197
(12:3:8)
Y ¼ g(X) ¼ 1=(X a)2 .
1. g(x) satisfies the restrictions.
pffiffiffi 2. Solving for x in y ¼ 1=(x a)2 , we obtain two roots x1 ¼ a (1= y), pffiffiffi x2 ¼ a þ (1= y).
1
1
3.
dg(x)
d 3=2 ¼ ¼ 2
dx dx (x a)2 (x a)3 ¼ j2y j 1 1 1 4. fY ( y) ¼ 3=2 fX a pffiffiffi þ fX a þ pffiffiffi ; y . 0 (12:3:9) 2y y y Example 12.3.4 (See Example 12.2.8) The probability density functions fX(x) and g(x) and are given by 2 3 2 1 if 1:5 , x 3 5 g(x) ¼ 2 for all x fX (x) ¼ 4 9 x 1 0 otherwise Even though g(x) is defined from 21 to 1, the range of fX(x) plays a critical role in determining fY ( y). Figure 12.2.12 is redrawn in Fig. 12.3.3 to indicate the ranges. 1. g(x) satisfies the restrictions.
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2. Solving for x in pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi y ¼ 1/(x 2 1), we obtain two roots: x1 ¼ 1 þ (1=y), x2 ¼ 1 þ (1=y).
sffiffiffiffiffiffiffiffiffiffiffi
2
3. dg(x) d 1 x 1
dx ¼ dx (x2 1) ¼ 2 (x2 1)2 ¼
2y 1 þ y
4. fY ( y) have to be calculated in the four ranges of y as shown in Fig. 12.3.3. The density function fY ( y) is calculated as follows: Region I: y 21, two points of intersection: 1 2 2 2 1 þ
rffiffiffiffiffiffiffiffiffiffiffi
rffiffiffiffiffiffiffiffiffiffiffi
fY ( y) ¼ ¼
9 2
1 9 9 1
2y2
1 þ
y 1 þ
y y 1 Region II: 1 , y , no points of intersection: 8 fY ( y) ¼ 0 1 4 Region III: , y , one point of intersection: 8 5 1 2 1 1 fY ( y) ¼
rffiffiffiffiffiffiffiffiffiffiffi
rffiffiffiffiffiffiffiffiffiffiffi
¼
9 2
1 9 1
2y2
1 þ
y 1 þ
y y
198
Functions of a Single Random Variable
FIGURE 12.3.3
4 Region IV: y . , two points of intersection: 5 1 2 2 2 1 þ fY ( y) ¼
rffiffiffiffiffiffiffiffiffiffiffi
rffiffiffiffiffiffiffiffiffiffiffi
¼
9 9
9 1 1
2y2
1 þ
y2
1 þ
y y Here we have found the result far more easily than in Example 12.2.8. Example 12.3.5 (See Example 12.2.10) by g(u) ¼ sec(u) and fQ(u) is given by " 1 fQ (u) ¼
2p 0
In Example 12.2.10, the function g(u) is given
for 0 , u 2p
#
otherwise
We will now find fX(x) directly from fQ(u). 1. g(u) satisfies the restrictions. 2. Solving for u in x ¼ sec(u), we have u1 ¼ sec21(x) and u10 ¼ 2p 2 sec21(x).
pffiffiffiffiffiffiffiffiffiffiffiffiffi
3.
dg(u)
d
¼ j sec (u) tan (u)j ¼
x x2 1
¼ sec (u)
du du 4. From Fig. 12.2.19, the regions of x are (a) x 21, (b) 2 1 , x 1, (c) x . 1. (a) x 21, two points of intersection: 1 1 1 1 fX (x) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi x x2 1 2p 2p px x2 1 (b) 21 , x 1, no points of intersection, fX(x) ¼ 0.
199
12.3 Direct Determination of Density fY ( y) from fX (x)
(c) x . 1, two points of intersection: 1 1 1 1 þ fX (x) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2p 2p x x 1 px x2 1 These are the same results obtained in Example 12.2.10.
Example 12.3.6 (See Example 12.2.11) " 1 fQ (u) ¼ 2p 0
Here g(u) ¼ cos(u) and fQ(u) is given by for p , u p
#
otherwise
The three regions of x are (a) x 2 1, (b) 21 , x 1, (c) x . 1. 1. g(u) satisfies the restrictions. 2. The solutions for x ¼ cos(u) are u1 ¼ 2cos21(x) and u10 ¼ cos21(x).
pffiffiffiffiffiffiffiffiffiffiffiffiffi
3.
dg(u)
d
¼ cos (u) ¼ j sin (u)j ¼
1 x2
du du 4. fX(x) can now be found in the three regions. (a) x 21, no points of intersection: fX (x) ¼ 0: (b) 21 , x 1, two points of intersection: 1 1 1 1 þ fX (x) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2p 2p 1x p 1 x2
(12:3:10)
(c) x . 1, no points of intersection: fX (x) ¼ 0: These are the same results obtained in Example 12.2.11.
Example 12.3.7 This example will have relevance in the next section. Here g(x) and fX(x) are given by 2
0
6 4 2 þ ln g(x) ¼ 6 4 5x 0
3
if
x1
if
7 1 , x , 57 5
if
x5
"1 fX (x) ¼
if
1,x5
#
4 0 otherwise
The functions g(x) and fX(x) are shown in Fig. 12.3.4. 1. g(x) satisfies the restrictions. 2. There are no points of intersection in the region y 2. Solving for y ¼ g(x) in the region y . 2, we obtain y ¼ 2 þ ln
4 4 ; y 2 ¼ ln 5x 5x
or 5 x ¼ 4e( y2)
200
Functions of a Single Random Variable
FIGURE 12.3.4
or x ¼ 5 4e( y2)
for
y.2
4
¼ 1 ¼ 4e( y2) ; 3. dg(x) ¼ d 2 þ ln
dx dx 5x 5x
y.2
4. Since there are no points of intersection of y with g(x) in the region y 2, it follows that fY ( y) ¼ 0. In the region y . 2, 1 fY ( y) ¼ 4e( y2) ¼ e( y2) 4
for y . 2
B 12.4 INVERSE PROBLEM: FINDING g(x) GIVEN fX(x) AND fY ( y) In the previous sections we studied finding fY ( y) given fX(x) and g(x). In this section we will find g(x) given fX(x) and fY ( y). We will use the conservation of probability measure given by an equation similar to Eq. (12.3.3): fY (h)Dh ¼ fX (j1 )Dj1 þ fX (j2 )Dj2 þ þ fX (jn )Djn
(12:4:1)
Taking the limit as Dh, Dj1, Dj2, . . . , Djn ! 0, all the jk coalesce to j, and integrating h between 21 to y and j between 21 to x, we obtain ðy ðx fY (h)dh ¼ fX (j)dj (12:4:2) 1
1
The lefthand side of Eq. (12.4.2) is a function of y, the righthand side is a function of x, and the equation can be solved for y, which yields g(x) as the transforming function. Example 12.4.1 Given fX (x) ¼ 2e2(x2) u(x 2) and fY ( y) ¼ 14 ½u( y 2) u( y 6), we have to find the transforming function g(x). The functions fX(x) and fY ( y) are shown in Fig. 12.4.1.
12.4
Inverse Problem: Finding g(x) Given fX(x) and fY ( y)
201
FIGURE 12.4.1
Integrating both fX(x) and fY ( y), we obtain the following: ðy ðy 1 y2 dh ¼ for fY (h)dh ¼ 4 4 2 2 ðx ðx fX (j)dj ¼ 2e2(j2) dj ¼ 1 e(x2) for 2
2,y6 x.2
2
Thus y2 ¼ 1 e(x2) 4
for
2 , y 6 and x . 2
or y ¼ g(x) ¼ 6 4e(x2)
for
x.2
The function g(x) is graphed in Fig. 12.4.2. Using the same techniques as in the previous section, we can show that the transformation function g(x) indeed gives a uniform distribution in (2,6], given that fX(x) is an exponentially distributed function starting at y ¼ 2. Example 12.4.2 (See Example 12.3.7) We will revisit Example 12.3.7, where fX(x) is uniformly distributed in the interval (1,5] and fY ( y) is exponentially distributed as
FIGURE 12.4.2
202
Functions of a Single Random Variable
e 2( y22) starting at y ¼ 2. We have to find the transforming functions. Proceeding as in the previous example, we have ðy ðx 1 x1 (h2) dj or 1 e( y2) ¼ , y . 2, x . 1 e dh ¼ 4 4 2 1 or e( y2) ¼
5x , 4
y . 2, x . 1
Taking logarithm on both sides, we obtain 5x 4 ( y 2) ¼ ln or y ¼ g(x) ¼ 2 þ ln , 4 5x
x.1
g(x) is exactly the same function that we used in Example 12.3.7 and is shown in Fig. 12.3.4.
B 12.5 MOMENTS OF A FUNCTION OF A RANDOM VARIABLE Expectation
We will now find the expectation of the random variable Y ¼ g(X ). We have already found the density function fY ( y). We can use the usual definition of expectations and write ð1 mY ¼ E½Y ¼ y fY ( y)dy (12:5:1) 1
However, if only the expected value of Y, and not the density function fY ( y) is required, we can find the result from the knowledge of fX(x) and g(x). Using the law of conservation of probability measure [Eq. (12.3.2)], we have ð1 ð1 E½Y ¼ y fY ( y)dy ¼ y fX (x)dx (12:5:2) 1
1
Substituting y ¼ g(x) in Eq. (12.5.2), we obtain the equation for E[Y ] in terms of the density function fX(x): ð1 E½Y ¼ g(x)fX (x)dx (12:5:3) 1
If the random variable is of the discrete type, then X X E½Y ¼ yi P( y ¼ yi ) ¼ g(xi ) P(x ¼ xi ) i
(12:5:4)
i
Variance
In a similar manner, the variance of Y ¼ g(X ) can be defined as ð1 2 2 sY ¼ E½Y mY ¼ ( y mY )2 fY ( y) dy ð1 ¼ 1
1
( y mY )2 fX (x)dx ¼
ð1 1
½g(x) mY 2 fx (x)dx
(12:5:5)
12.5
Moments of a Function of a Random Variable
203
Higher-Order Moments
The rth moment of Y ¼ g(X ) can be defined as follows: mr ¼ E½Yr ¼ ð1
ð1
( y)r fY ( y)dy
1 r
ð1
( y) fX (x)dx ¼
¼ 1
½g(x)r fX (x)dx
(12:5:6)
1
Example 12.5.1 We will take Example 12.2.7 and calculate the expected values by using the density functions fY ( y) and fX(x). Function fX(x) is uniformly distributed in (22, 2] as shown in Fig. 12.2.10. The density function fY ( y) for that example is given by 1 3 4 fY ( y) ¼ d( y þ 1) þ u y u( y) 2 8 3 and g(x), fX(x), and fY ( y) are shown in Fig. 12.5.1. Using Eq. (12.5.1) to obtain E[Y ], we have
1 3 4 d( y þ 1) þ u y u( y) dy 2 8 3 1 ð 4=3 1 3 1 3 16 1 y dy ¼ þ ¼ ¼ þ 2 8 2 8 9:2 6 0 ð1
E½Y ¼
y
FIGURE 12.5.1
204
Functions of a Single Random Variable
Using Eq. (12.5.3) to obtain E[Y ], we have ð ð2 1 0 2 x dx E½Y ¼ 1 dx þ 4 2 03 2 1 2 4 1 1 1 ¼ þ ¼ þ ¼ 4 4 3 2 2 3 6 This result could just as well have been obtained by inspection of g(x) by adding the areas of the rectangle (22) and the triangle 43 and dividing them by 14, yielding 16. All three of these methods yield exactly the same result. We will now compute the variance of Y from fY ( y) using Eq. (12.5.5): ð1 1 2 1 3 4 d( y þ 1) þ u y yþ var½Y ¼ u( y) dy 6 2 8 3 1 2 ð 4=3 1 5 3 1 2 25 91 83 yþ ¼ ¼ þ dy ¼ þ þ 2 6 6 72 216 108 0 8 Computing var[Y ] from fX(x) and using var[Y ] ¼ E[Y ]2 2 m2Y, we have ð ð 1 0 1 2 2 2 1 2 x dx var½Y ¼ (1) dx þ 4 2 4 0 3 36 ¼
2 1 32 1 83 þ ¼ 4 4 27 36 108
and the two results are the same. Example 12.5.2 We are given fX(x) and fY ( y), and we have to find g(x) and the expectation and variance of Y using fY ( y) and fX(x): 2 2 3 "1 # 3y 2,x6 ; fY ( y) ¼ 4 8 0 , y 2 5 fX (x) ¼ 4 0 otherwise 0 otherwise Integrating fX(j) from 2 to x and fY (h) from 0 to y, we have ðy 2 ðx 1 3h dj ¼ dh 4 2 0 8 1 y3 (x 2) ¼ ; 2 , x 6, 0 , y 2 4 8 Hence, y ¼ g(x) ¼ ½2(x 2)1=3 . We also note the following: Expectation with fY ( y): ð2 y
E½y ¼ 0
3y2 3 dy ¼ 2 8
Expectation with fX(x): ð6 E½ y ¼
1 21=3 3 ½2(x 2)1=3 dx ¼ 3 41=3 ¼ 4 2 4 2
12.5
Moments of a Function of a Random Variable
205
FIGURE 12.5.2
Variance with fY ( y): ð2 var½ y ¼ 0
3 y 2
2
3y2 3 dy ¼ 20 8
Variance with fX(x), using var[Y ] ¼ E[Y ]2 2 m2Y: ð6 1 9 3 9 3 var½ y ¼ ½2(x 2)2=3 dx ¼ 82=3 ¼ 4 4 5 4 20 2 Both methods give the same results. Indicator Functions
An indicator function IA(j) is a real-valued function defined on the sample space S, having a value 0 or 1 depending on whether the j point is in the event A , S. Or 1 j[A (12:5:7) IA (j) ¼ 0 jA The indicator function is a unit step for sets. It is shown in Fig. 12.5.2. The indicator function is a zero – one random variable, and if P{j [ A} ¼ p P{j A} ¼ 1 p ¼ q
(12:5:8)
then the expected value and the variance of the indicator function IA are E½IA ¼ 1 p þ 0 q ¼ p var½IA ¼ 12 p p2 ¼ pq
(12:5:9)
Properties of Indicator Functions 1. A , B () IA IB 2. IA ¼ IA2 ¼ ¼ IAn 3. Is ¼ 1: sample space 4. IAB ¼ min (IA , IB ) 6. IA ¼ 1 IA : A ¼ complement of A
(12.5.10)
CHAPTER
13
Functions of Multiple Random Variables
B 13.1 FUNCTION OF TWO RANDOM VARIABLES, Z 5 g(X,Y ) In the previous chapter we discussed a single function of a single random variable. Here we will find the probability fZ(z) of a single function Z of two random variables X and Y defined by the joint probability density function fXY(x, y). The analysis is very similar to that in the previous chapter. Following similar analysis given in Chapter 12, we have Z ¼ g(X, Y ) as a function that maps the domain space of Z given by fCxy,FXY,PXYg into the range space fRz,FZ ,PZg, which is a real line, as shown in Fig. 13.1.1 {Z ¼ g(X,Y)} : {Cxy ,FXY ,PXY } ! {Rz ,FZ ,PZ }
(13:1:1)
and the mapping of fX,Y g is from the probability space fS, F, Pg (where, S is the product space S ¼ SX SY) into the domain space of Z given by fCxy,FXY,PXYg X,Y: {S,F,P} ! {Cxy ,FXY ,PXY }
(13:1:2)
Alternately, if Dxy , Cxy and Iz , Rz, then Iz is the image of Dxy under the transformation g and Dxy is the inverse image of Iz under the transformation g – 1. Hence Iz is the set of all points (x,y) belonging to Dxy such that g(x,y) belongs to Iz. This is represented by Dxy ¼ {x,y : g(x,y) [ Iz }
(13:1:3)
We also have conditions similar to those in the last chapter for Z to be a random variable. Conditions on Z 5 g(X,Y) to Be a Random Variable 1. The domain of g(x,y) must include the range of the RV Z. 2. For each z [ Iz, fg(x,y) zg must form a field FZ; that is, it must consist of the unions and intersections of a countable number of intervals. 3. The probability of the event fg(x,y) ¼ +1g is 0.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
206
Function of Two Random Variables, Z 5 g(X,Y )
13.1
207
FIGURE 13.1.1
In essence, the transformation Z ¼ g(X,Y ) induces a probability measure PZ on FZ , and we have to find this probability measure. From Fig. 13.1.1, we can write PZ (Z) ¼ P(Z [ Iz ) ¼ PXY (g1 (Z) [ Dxy ) ¼ PXY (X,Y [ Dxy )
(13:1:4)
The set Iz consists of all the countable unions and intersections of the type a , Z b. Without loss in generality, we will assume that Iz ¼ fZ zg, and substituting for Dxy from Eq. (13.1.3) in Eq. (13.1.4), we have PZ (Z) ¼ PXY ½(X,Y) [ Dxy ¼ PXY ½x,y:g(x,y) [ Iz ¼ PXY ½x, y:g(x,y) z ¼ FXY (z) ¼ P(Z z) ¼ FZ (z)
(13:1:5)
Equation (13.1.5) can also be written as ð
ð
PZ (Z) ¼ PXY ½(X,Y) [ Dxy ¼ FZ (z) ¼
fXY (j,h)dj dh
(13:1:6)
Dxy
where fXY (x,y) is the joint density of the random variables X and Y. In conclusion, we can say that the probability of Z z is given by the probability of the set of all values of x and y such that g(x,y) z, or by integrating the joint density fXY (x,y) over the domain Dxy. We will use both these interpretations in solving problems. Summary of Steps for Finding FZ(z) 5 P(Z z) 1. Graph the function g(x,y) in the x– y plane. 2. Equate g(x,y) ¼ z 3. Find Dxy, the distinct regions in the x– y plane such that g(x,y) z. 4. Find FZ(z) from Eq. (13.1.5) or (13.1.6). Example 13.1.1 Given Z ¼ g(X,Y ) ¼ min(X,Y ), we will find FZ(z) given the joint distribution FXY(x,y). We follow the scheme outlined above. 1. g(x,y) is shown in Fig. 13.1.2. 2. g(x,y) ¼ z is marked on the diagram. 3. The region Dxy is the set of all points in the x –y plane such that either x z or y z. It is shown by the region ABHCDF in Fig. 13.1.2.
208
Functions of Multiple Random Variables
FIGURE 13.1.2
4. The probability of Dxy is given by Pf(X z) or (Y z)g, which can be written as Pfrectangle ABEFg þ Pfrectangle GCDFg 2 Pfrectangle GHEFg. We subtract Pfrectangle GHEFg since it occurs twice in the addition of the first two terms. Hence FZ (z) ¼ FY (z) þ FX (z) FXY (z,z)
(13:1:7)
If X and Y are independent, then Eq. (13.1.7) becomes FZ (z) ¼ FY (z) þ FX (z) FX (z)FY (z)
(13:1:8)
and the density function fZ(z) is given by fZ (z) ¼ fY (z) þ fX (z) fX (z)FY (z) FX (z)fY (z)
(13:1:9)
We will define the joint density function fXY (x,y) in the region f0 , x 4g, f0 , y 2g as shown in Fig. 13.1.3 and find the distribution FZ(z).
FIGURE 13.1.3
13.1
Function of Two Random Variables, Z 5 g(X,Y )
209
The joint density fXY(x,y) and the distribution FXY(x,y) are shown below: 2 0 for x 0, y 0 61 fXY (x,y) ¼ 4 for 0 , x 4, 0 , y 2 ; 8 0 for x . 0, y . 0 2 0 for x 0, y 0 6 xy for 0 , x 4, 0 , y 2 6 68 6x FXY (x,y) ¼ 6 6 4 for 0 , x 4, y . 2 6y 6 for x . 4, 0 , y 2 4 2 1 for x . 0, y . 0 1. In region I, for fz 0g, FZ(z) ¼ 0. 2. In region II, f0 , z 2g or f0 , x 2g, f0 , y 2g we have the following formula from the equation derived earlier: FZ (z) ¼
z z z2 z þ ¼ (6 z) 4 2 8 8
for
0,z2
3. In region III, f2 , z 4g or f2 , x 4g, f2 , y 4g FZ (z) ¼ FX (z) þ FY (2) FXY (z,2) ¼
z z þ1 1¼1 4 4
for
2,z4
The same result can also be obtained from the fact that the random variable Y is always less than 2. Since the region Dxy is either X z or Y z, and z . 2, we have FZ(z) ¼ 1. 4. In region IV, fz . 4g or fx . 4g, fy . 4g, FZ(z) ¼ 1. The density function fZ(z) obtained by differentiating FZ(z) is shown below. 2 0 for z 0 63 z 6 for 0 , z 2 fZ (z) ¼ 6 64 4 for 2 , z , 4 40 0 for z . 4 Example 13.1.2 Given Z ¼ g(X,Y ) ¼ max(X,Y ), we will find FZ(z) given the joint distribution FXY(x,y). We follow the scheme outlined above. 1. g(x,y) is shown in Fig. 13.1.4. 2. g(x,y) ¼ max(x,y) ¼ z is marked on the diagram. 3. The region Dxy is the set of all points in the x –y plane such that x z and y z. This region is shown in Fig. 13.1.4. 4. The probability of Dxy is given by Pf(X,Y z)g, which can be written as FZ (z) ¼ FXY (z,z)
(13:1:10)
210
Functions of Multiple Random Variables
FIGURE 13.1.4
If X and Y are independent, then Eq. (13.1.10) becomes FZ (z) ¼ FX (z)FY (z)
(13:1:11)
and the density function fZ(z) is given by fZ (z) ¼ fX (z)FY (z) þ FX (z)fY (z)
(13:1:12)
We will use the same joint density function fXY(x,y) in the region f0 , x 4g, f0 , y 2g as shown in Fig. 13.1.3 and find the distribution FZ(z). 1. In region I, for fz 0g, FZ(z) ¼ 0. 2. In region II, f0 , z 2g or f0 , x 2g, f0 , y 2g, FZ(z) is given by FZ (z) ¼
z2 8
for
0 , x 2, 0 , y 2
3. In the region III, f2 , z 4g or f2 , x 4g, f2 , y 4g we have z 2 z FZ (z) ¼ FXY (z, 2) ¼ ¼ 4 2 4
for
2 , x 4, 2 , y 4
We can also integrate the joint density function fXY(x,y) in the ranges f0 , x zg and f0 , y 2g: ðz ð2 FZ (z) ¼ 0
1 dy dx ¼ 08
ðz
2 z dx ¼ 8 4 0
for
2 , x 4, 2 , y 4
4. In region IV, fz . 4g or fx . 4g, fy . 4g, FZ(z) ¼ 1.
13.1
Function of Two Random Variables, Z 5 g(X,Y )
211
The density function fZ(z) obtained by differentiating FZ(z) is shown below. 2 0 for z 0 6 z for 0 , z 2 6 64 fZ (z) ¼ 6 1 6 for 2 , z , 4 4 4 0 for z . 4 Example 13.1.3 edge of fXY(x,y).
Given that Z ¼ g(x,y) ¼ X þ Y, we have to find FZ(z) from the knowl-
1. The function g(x,y) is shown in Fig. 13.1.5. 2. The equation x þ y ¼ z is shown in Fig. 13.1.5. 3. Dxy is shown as the shaded region in the figure. 4. The distribution function FZ(z) is fZ (z) ¼ fX (z)FY (z) þ FX (z)fY (z)
(13:1:13)
The density function fZ(z) is obtained by differentiating FZ(z), and the result is ð 1 ð zx ð1 ð zx d d fZ (z) ¼ fXY (x,y)dy dx ¼ f (x,y)dy dx dz 1 1 1 dz 1 (13:1:14) ð1 ¼ fXY (x,z x)dx 1
If X and Y are independent in Eq. (13.1.14), then ð1 fX (x)fY (z x)dx fZ (z) ¼
(13:1:15)
1
Equation (13.1.15) is a convolution integral, and we can use characteristic functions to write FZ (v) ¼ FX (v)FY (v) fZ(z) can obtained from FZ(v).
FIGURE 13.1.5
(13:1:16)
212
Functions of Multiple Random Variables
Example 13.1.4 We are given X and Y as independent random variables with Z ¼ g(X,Y ) ¼ 2X 2 12Y. The density functions fX(x) and fY( y) are given by fX (x) ¼ 2e2x u(x);
1 fY (y) ¼ ey=2 u(y) 2
We have to find fZ(z). Substituting U ¼ 2X and V ¼ 12Y, we can obtain the corresponding densities of U and V from Eq. (12.3.7): 1 u fU (u) ¼ fX ¼ eu u(u); fV (v) ¼ 2fY (2v) ¼ ev u(v) 2 2 With this substitution, Z can be given by Z ¼ U þ (2V ). Since U and V are also independent, we can use Eq. (13.1.16) to find fZ(z). The characteristic functions of U and (2V ) are FU (v) ¼
1 1 ; FV (v) ¼ 1 jv (1 þ jv)
Using the independence property, we obtain 1 1 1 1 ¼ þ FZ (v) ¼ FU (v)FV (v) ¼ (1 jv)(1 þ jv) 2 1 jv 1 þ jv Taking the inverse transform, we obtain 1 1 fZ (z) ¼ ½ez u(z) þ ez u(z) ¼ ejzj 2 2 The functions fX(x), fY( y), and fZ(z) are shown in Fig. 13.1.6.
FIGURE 13.1.6
13.1
Example 13.1.5
Function of Two Random Variables, Z 5 g(X,Y )
213
Two independent random variables X and Y are distributed normally as
2 1 fX (x) ¼ pffiffiffiffiffiffiffiffiffiffiffiffi e(1=2)½(x2)=3 ; 2p 3
2 1 fY (y) ¼ pffiffiffiffiffiffiffiffiffiffiffiffi e(1=2)½(y3)=4 2p 4
Another random variable Z is given by Z ¼ X 2 Y. We have to find fZ(z). The characteristic functions of X and (2Y ) are given by FX (v) ¼ e jv
2
½ðv2 9Þ=2
; FY (v) ¼ ejv3½ðv
2
16Þ=2
Using the independence of X and Y, we have 2 2 3 2 2 FZ (v) ¼ e jv ½ðv 9Þ=2 e jv ½ðv 16Þ=2 ¼ ejv½ðv 25Þ=2 and fZ(z) is also normally distributed with mean 21 and variance 25, and is given by 2 1 fZ (z) ¼ pffiffiffiffiffiffiffiffiffiffiffiffi e(1=2)½(x1)=5 2p 5
Hence, the probability of a sum of independent Gaussian random variables is also a Gaussian random variable with mean equaling the sum of means, and variance equaling the sum of variances.
Example 13.1.6 This example illustrates when the two random variables X and Y are not independent but are given by a joint Gaussian density function: ( ) 1 1 2 2 fXY (x,y) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi exp pffiffiffiffiffiffiffiffiffiffiffiffiffi ½x 2rxy þ y (13:1:17) 2p 1 r2 2 1 r2 where the correlation coefficient is r and the random variable Z ¼ X þ Y. Here we can use Eq. (13.1.14) only. Substituting fXY (x,y) into Eq. (13.1.14), we have ð1 fZ (z) ¼
fXY (x,z x)dx 1
1 2 2 ½x 2rx(z x) þ (z x) dx exp 2(1 r2 ) 1 ð1 1 1 2 2 2 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ½2x exp 2rxz þ 2rx þ z 2zx dx 2(1 r2 ) 2p 1 r2 1 ð1 1 1 2 2 ½2x ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi exp (1 þ r) 2xz(1 þ r) þ z dx 2(1 r2 ) 2p 1 r2 1 ð1 1 2 2 z2 =2(1r2 ) p ffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ e½(1þr)=(1r )½x xz dx e 2 2p 1 r 1 ð1 1 2 2 2 e½1=(1r)½x xz dx ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ez =2(1r ) 2 2p 1 r 1
1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 2p 1 r2
ð1
(13:1:18)
214
Functions of Multiple Random Variables
Completing squares under the integral sign in Eq. (13.1.18), we have ð1 1 1 h z i 2 z2 z2 =2(1r2 ) x fZ (z) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi e exp dx 1r 2 4 2p 1 r2 1 ð1 1 1 h z i2 2 2 2 x exp ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi e½z =2(1r )þ½z =4(1r) dx 1r 2 2p 1 r2 1 ( ) ð1 1 z2 =4(1þr) 1 1 x (z=2) 2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffi e exp dx 2 (1 r)=2) 2p 2pð1 r2 Þ 1 ( ) ð1 1 z2 =4(1þr) 1 1 1 x (z=2) 2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp dx ¼ pffiffiffiffiffiffi e 2 (1 r)=2) 2(1 þ r) 2p 1 r 1 2p 2 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} ¼1
1 1 2 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi ez =4(1þr) 2(1 þ r) 2p
(13:1:19)
Thus, we see that the sum of jointly Gaussian-distributed zero mean, unit variance random variables with correlation coefficient r is also a zero mean Gaussian with variance 2(1þ r). If r ¼0, we have independent Gaussian random variables and variance equals 2, which is the sum of unit variances. If r ¼ 1, we have essentially Z ¼ 2X and the variance is 4. Example 13.1.7 Two independent Gaussian random variables X and Y are with zero mean and variance s2. The probability density functions of X and Y are 1 1 2 2 (13:1:20) fX (x) ¼ pffiffiffiffiffiffi e(x=2s ) ; fY (x) ¼ pffiffiffiffiffiffi e(y=2s ) s 2p s 2p pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Another random variable Z is given by Z ¼ g(X,Y) ¼ X 2 þ Y 2 . We have to find the pdf pffiffiffiffiffiffiffiffiffiffiffiffiffiffi fZ(z). The region Dxy ¼ x2 þ y2 z is shown in Fig. 13.1.7. Using Eq. (13.1.6), we have ðð
FZ (z) ¼
1 (j2 þh2 Þ=2s2 e dj dh 2 2p s pffiffiffiffiffiffiffiffiffi 2 2 x þy z
We can solve this integral by using polar coordinates using the following substitution: j ¼ r cos u; h ¼ r sin u; dj dh ¼ r dr du;
u ¼ tan1
h j
Thus ð 2p ð z
1 (r2 =2s2 ) e r dr du ¼ 2 0 0 s 2p h i 2 2 ¼ 1 e(z =2s ) u(z)
FZ (z) ¼
ðz
r (r2 =2s2 ) e dr 2 0s (13:1:21)
13.1
Function of Two Random Variables, Z 5 g(X,Y )
215
FIGURE 13.1.7
and fZ (z) ¼
z (z2 =2s2 ) e u(z) s2
(13:1:22)
where u(z) is the usual unit step function. This is a Rayleigh distribution as given in Eq. (7.8.5) or a chi-square distribution with 2 degrees of freedom. Example 13.1.8 This example is similar to the previous example except that Z ¼ g(X,Y) ¼ X 2+Y 2. The region Dxy ¼ x 2+y 2 z. We can now find the pdf fZ(z) using polar coordinates as in the previous example. Thus ð 2p ð pffiz ð pffiz 1 (r2 =2s2 ) r (r2 =2s2 ) e r dr du ¼ e dr FZ (z) ¼ 2 2 0 s 2p 0 s 0 h i 2 ¼ 1 e(z=2s ) u(z) (13:1:23) and the density function fZ(z) is given by 1 z=2s2 e fZ (z) ¼ u(z) 2s2
(13:1:24)
This is an exponential distribution or a chi-square distribution given by Eq. (7.7.3) with 2 degrees of freedom. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The density functions for Z ¼ X 2 þ Y 2 and Z ¼ X 2 þ Y 2 given by Eqs. (13.1.22) and (13.1.24) are shown in Fig. 13.1.8 for comparison. Example 13.1.9 In Example 13.1.7 we assumed that the independent random variables X and Y are of zero mean. In this example, we will assume that X and Y have the same variance s2 and that their mean values are mX and mY respectively. The density functions are 1 2 fX (x) ¼ pffiffiffiffiffiffi e½(xmx )=2s ; s 2p
1 2 fY (y) ¼ pffiffiffiffiffiffi e½(ymy )=2s s 2p
(13:1:25)
216
Functions of Multiple Random Variables
FIGURE 13.1.8
We will now find fZ(z) for Z ¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 2 þ Y 2 . Function FZ(z) is now given by
1 (j mX )2 þ (h mY )2 exp dj dh; s2 2p 2s2 pffiffiffiffiffiffiffiffiffi 2 2 ðð
FZ (z) ¼
z.0
(13:1:26)
x þy z
Expanding Eq. (13.1.26) we have, 1 j2 þ h2 2(mX j þ mY h) þ m2X þ m2Y exp FZ (z) ¼ dj dh; s2 2p 2s2 pffiffiffiffiffiffiffiffiffi 2 2 ðð
z.0
x þy z
(13:1:27) Substituting polar coordinates in Eq. (13.1.27), we obtain the following: 2 1 r 2r(mX cos u þ mY sin u) þ m2X þ m2Y exp r dr du 2 2s2 0 0 s 2p 2 2 2 ð e(mX þmY )=s z ð1=2Þðr=sÞ2 e ¼ s2 0 ð 2p 1 r(mX cos u þ mY sin u) exp du r dr; z . 0 2p 0 s2 ð 2p ð z
FZ (z) ¼
(13:1:28)
We now define qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m2X þ m2Y cos (c); mX ¼ c ¼ tan
1
mY mX
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m2X þ m2Y sin (c) mY ¼ ð13:1:29Þ
Function of Two Random Variables, Z 5 g(X,Y )
13.1
and a noncentrality parameter m ¼ (13.1.29), we have
217
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m2X þ m2Y . Substituting m in Eqs. (13.1.28) and
rm( cos u cos c þ mY sin u sin c) e exp du r dr s2 0 0 ð 2p 2 2 ð em =s z (1=2)ðr=sÞ2 1 rm( cos u c) e exp du r dr; z . 0 (13:1:30) ¼ 2p 0 s2 s2 0 2
em =s FZ (z) ¼ s2
2
ðz
(1=2)ðr=sÞ2
1 2p
ð 2p
The integral under u is the modified Bessel function of zero order with argument rm=s2 given by I0
ð 2p rm 1 rm( cos uc)=s2 e du ¼ s2 2p 0
Substituting this value in Eq. (13.1.30) and differentiating with respect to z, we obtain zm z 2 2 2 fZ (z) ¼ 2 e½(z þm )=s I0 2 ; z . 0 (13:1:31) s s This is Rice’s distribution as given in Eq. (7.8.11). With m ¼ 0, we get the Rayleigh distribution. Example 13.1.10 In this example we have Z ¼ g(X,Y ) ¼ XY. The function g(x,y) ¼ xy ¼ z is shown in Fig. 13.1.9 for z . 0. We have to find fZ(z) given the joint density fXY(x,y). The distribution function FZ(z) is obtained by integrating fXY(x,y) in the shaded region given by g(x,y) z. Since the region Dxy ¼ fxy zg is bounded by two disjoint curves, we have to integrate fXY (x,y) along the two regions f –1 , x 0g and f0 , x 1g. Hence ðð FZ (z) ¼ fXY (x,y)dy dx xyz
ð0 ð1
ð 1 ð z=x fXY (x,y)dy dx þ
¼ 1
z=x
fXY (x,y)dy dx 0
FIGURE 13.1.9
1
(13:1:32)
218
Functions of Multiple Random Variables
We differentiate Eq. (13.1.32) using the chain rule of differentiation and obtain fZ(z): ð1 z 1 z 1 fXY x, dx þ fXY x, dx x x x x 1 0 ð1 1 z fXY x, dx; z . 0 ¼ x 1 jxj ð0
fZ (z) ¼
(13:1:33)
It can be shown by similar techniques that Eq. (13.1.33) is valid for z 0. If X and Y are independent, then ð1
z 1 fX (x)fY dx x 1 jxj
fZ (z) ¼
for all z
(13:1:34)
We note that Eq. (13.1.34) is not a convolution integral even if the product of independent random variables is involved. The problem in Example 13.1.10 can also be solved using the techniques developed in Chapter 12. We will first find the conditional density fZ(z j X ¼ x), given the conditional density fY( y j X ¼ x) with Z ¼ xY. Since x is a scaling factor for the random variable Y, we can use Eq. (12.3.7) and obtain fZ (z j X ¼ x) ¼
1 z fY j X ¼ x jxj x
Hence, the unconditional density fZ(z) is given by ð1
1 z fY j X ¼ x fX (x)dx ¼ fZ (z) ¼ x 1 jxj
ð1
z 1 fXY x, dx x 1 jxj
which is the same as before. Example 13.1.11 We will now assume that X and Y are independent random variables with identical Cauchy distributions given by fX (x) ¼
2 2 ; fY (y) ¼ 2 p ð y þ 4Þ þ 4Þ
pðx2
Substituting these values in Eq. (13.1.34), we have ð1 fZ (z) ¼
1 2 2 dx 2 2 þ 4Þ jxj p x ð z 1 p 2þ4 x
Since fZ(z) is an even function, we can rewrite this equation as fZ (z) ¼
42 p2
ð1 0
x dx ðx2 þ 4Þðz2 þ 4x2 Þ
13.1
Function of Two Random Variables, Z 5 g(X,Y )
219
Substituting w ¼ x 2 and dw ¼ 2x dx and expanding into partial fractions, we have 4 p2
ð1
1 dw 2 0 ðw þ 4Þðz þ 4wÞ ð 4 1 1 1 1 ¼ 2 dw p 0 ðz2 16Þ ðw þ 4Þ ðz2 þ 4wÞ
fZ (z) ¼
1
1 4 4 wþ4 2
ln(w þ 4) ln(z þ 4w) 0 ¼ 2 2 ln 2 ¼ 2 2 p ðz 16Þ p ðz 16Þ z þ 4w 0 1
4 wþ4 4 1 4
ln 2 ln ¼ 2 2 ¼ ln 2 p ðz 16Þ z þ 4w 0 p2 ðz2 16Þ 4 z
or fZ (z) ¼
2 4 z ln p2 ðz2 16Þ 16
and fZ(z) is shown in Fig. 13.1.10. Example 13.1.12 Given Z ¼ g(X,Y ) ¼ Y/X, we have to find fZ(z) from the knowledge of the joint density function fXY(x,y). The region Dxy(x,y) ¼ fx,y: y xzg is shown in Fig. 13.1.11. In the two regions of Dxy given by (1) x 0 and (2) x . 0, FZ(z) can be written as follows: ðð FZ (z) ¼
fXY (x,y)dy dx yxz
ð0 ð1
ð 1 ð xz fXY (x,y)dy dx þ
¼ 1 xz
fXY (x,y)dy dx 0
FIGURE 13.1.10
1
(13:1:35)
220
Functions of Multiple Random Variables
FIGURE 13.1.11
Using the chain rule of differentiation in Eq. (13.1.35), we can obtain fZ(z) as ð0
ð1
fZ (z) ¼ ð1
fXY (x,xz)x dx þ
fXY (x,xz)x dx 0
1
jxjfXY (x,xz)dx
¼
(13:1:36)
1
As an illustration, we will find fZ(z) given that X and Y are jointly Gaussian random variables with means 0 and variances s2X and s2Y with correlation coefficient r, given by fXY (x,y) ¼
2 1 1 x xy y2 pffiffiffiffiffiffiffiffiffiffiffiffiffi exp 2r þ 2(1 r2 ) s2X sX sY s2Y 2psX sY 1 r2
Substituting fXY(x,y) into Eq. (13.1.36) and using the fact that fXY(x,y) is an even function we have, 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi fZ (z) ¼ 2psX sY 1 r2
ð1 0
x2 1 z z2 x exp 2r þ dx sX sY s2Y 2(1 r2 ) s2x
1 s2 s2 (1 r2 ) pffiffiffiffiffiffiffiffiffiffiffiffiffi 2 X Y 2 psX sY 1 r2 sY 2rsX sY þ z2 sX pffiffiffiffiffiffiffiffiffiffiffiffiffi sX sY 1 r2 ¼ sY s2Y þ 2 ps2X z2 2rz s X sX
¼
(13:1:37)
By completing the squares in the denominator of Eq. (13.1.37), we can write pffiffiffiffiffiffiffiffiffiffiffiffiffi sX s Y 1 r2 fZ (z) ¼ 2 p s2X z r ssXY þ s2Y ð1 r2 Þ
(13:1:38)
13.1
Function of Two Random Variables, Z 5 g(X,Y )
221
FIGURE 13.1.12
This is a Cauchy distribution centered at z ¼ r(sY/sX) and is shown in Fig. 13.1.12 with s2X ¼ 4, s2Y ¼ 9, and r ¼ 35 : Integrating Eq. (13.1.38) with respect to z, we obtain the distribution function FZ(z) as " # 1 1 1 sX z rsY pffiffiffiffiffiffiffiffiffiffiffiffiffi FZ (z) ¼ þ tan 2 p sY 1 r 2
(13:1:39)
Example 13.1.13 We are given two independent random variables, X and Y, which are exponentially distributed as fX (x) ¼ 2e2x u(x);
fY (x)ey u( y)
We have to find the density function fZ(z) of the random variable given by Z ¼ (Y/X ). From Eq. (13.1.36), we have ð1 fZ (z) ¼
jxjfXY (x,xz)dx 1 ð1
x 2e
¼
2x
e
xz
ð1 dx ¼
0
¼
2xex(2þz) dx
0
2 (2 þ z)2
Integrating fZ (z), we obtain the distribution FZ (z) as FZ (z) ¼ 1 These functions are shown in Fig. 13.1.13.
2 u(z) 2þz
222
Functions of Multiple Random Variables
FIGURE 13.1.13
B 13.2 TWO FUNCTIONS OF TWO RANDOM VARIABLES, Z 5 g(X,Y ), W 5 h(X,Y ) We are given two random variables, X and Y, and their joint density function fXY(x,y). We form the functions Z ¼ g(X,Y ) and W ¼ h(X,Y ) and determine the joint density fZW(z,w) from knowledge of fXY (x,y). The analysis is very similar to the one presented in the previous section. The functions Z ¼ g(X,Y ) and W ¼ h(X,Y ) map the domain space of fZ,Wg given by fCxy, FXY, PXYg into the range space fCzw, FZW, PZWg as shown in Fig. 13.2.1. From Fig. 13.2.1, we obtain Z ¼ g(X,Y), W ¼ h(X,Y);
{Cxy , FXY ,PXY } ! {Czw , FZW , PZW }
(13:2:1)
and the mapping of fX, Y g is from the probability space fS, F, Pg (where S is the product space S ¼ SX SY) into the domain space of fZ,Wg given by fCxy, FXY, PXYg: X,Y : {S, F, P} ! {Cxy , FXY , PXY }
(13:2:2)
Alternately, if Dxy , Cxy and Dzw , Czw, then Dzw is the image of Dxy under the transformation fg,hg and Dxy is the inverse image of Dzw under the transformation {g – 1, h – 1}. Hence, Dzw is the set of all points (x,y) belonging to Dxy such that g(x,y), h(x,y) belongs
FIGURE 13.2.1
13.2
Two Functions of Two Random Variables, Z 5 g(X,Y ), W 5 h(X,Y )
223
to Dzw. This is represented by Dxy ¼ {x,y:g(x,y),h(x,y) [ Dzw }
(13:2:3)
We also have conditions similar to the last section for Z and W to be a random variables. Conditions on Z 5 g(X,Y ) and W 5 h(X,Y ) to Be Random Variables 1. The domain of fg(x,y), h(x,y)g must include the range of the RV Z and W. 2. For each z,w , Dzw, the set fg(x,y) z, h(x,y) wg must form a field FZW; that is, it must consist of the unions and intersections of a countable number of intervals. 3. The probability of the events fg(x,y) ¼ +1g and fh(x,y) ¼ +1g is 0. In essence, the transformation fZ ¼ g(X,Y ), W ¼ h(X,Y )g induces a probability measure PZW on FZW, and we have to find this probability measure. From Fig. 13.2.1 we can write PZW (Z,W) ¼ P(Z,W [ Dzw ) ¼ PXY g1 (Z,W),h1 (Z,W) [ Dxy ) ¼ PXY X,Y [ Dxy
(13:2:4)
The set Dzw consists of all the countable unions and intersections of the type fa , Z bg, fc , W dg. Without loss of generality, we will assume that Dzw ¼ fZ z,W wg. Thus, substituting for Dxy from Eq. (13.2.3) in Eq. (13.2.4), we have PZW (Z,W) ¼ PXY X,Y [ Dxy ¼ PXY ½ x,y:g(x,y),h(x,y) [ Dzw ¼ PXY ½ x,y:g(x,y) z,h(x,y) w ¼ FXY (z,w) ¼ P(Z z,W w) ¼ FZW (z,w)
(13:2:5)
Equation (13.2.5) can also be written as PZW (Z,W) ¼ PXY (X,Y) [ Dxy ¼ FZW (z,w) ¼
ðð fXY (j,h)dj dh
(13:2:6)
Dxy
where fXY(x,y) is the joint density of the random variables X and Y. In conclusion, we can say that the probability of fZ z, W wg is given by the probability of the set of all values of x and y such that fg(x,y) z, h(x,y) wg or by integrating the joint density fXY(x,y) over the domain Dxy. We will use both these interpretations in solving problems. Example 13.2.1 Here we are given that Z ¼ max(X,Y ), W ¼ min(X,Y ) and we have to find FZW(z,w) from the knowledge of the joint density FXY(x,y). Both Z and W are shown in Fig. 13.2.2. The regions min(X,Y ) w ¼ fX wg < fY zg and max(X,Y ) z ¼ fX zg > X,Y ) z ¼ fX zg > fY zg are shown in Fig. 13.2.2. There are two regions of interest: w . z and w z. In the first region w . z, Dxy equals the semiinfinite square defined by the point B ¼ (z, z). Thus FZ,W (z,w) ¼ FXY (z,z) ¼ FZ (z):
w.z
(13:2:7)
224
Functions of Multiple Random Variables
FIGURE 13.2.2
In the second region w z, Dxy equals the semiinfinite square defined by the point B ¼ (z,z) minus the square ABCD. Hence FZ,W (z,w) ¼ FXY (z,z) P(ABCD) ¼ FXY (z,z) ½FXY (z,z) FXY (z,w) FXY (w,z) þ FXY (w,w) ¼ FXY (z,w) þ FXY (w,z) FXY (w,w); w z
(13:2:8)
When w ¼ z, Eq. (13.2.8) is identical to Eq. (13.2.7). We can obtain FW(w) by letting z tend to 1 in Eq. (13.2.8), yielding FZ,W (z,w) ¼ FXY (z,w) þ FXY (w,z) FXY (w,w) ¼ FX (w) þ FY (w) FXY (w,w) ¼ FW (w) which is similar to Eq. (13.1.7) derived in the previous section. If X and Y are independent, then Eqs. (13.2.7) and (13.2.8) become w.z FX (z)FY (z): FZ,W (z,w) ¼ FX (z)FY (w) þ FX (w)FY (z) FX (w)FY (w): w z
(13:2:9)
(13:2:10)
Example 13.2.2 To continue the previous example, the density functions of two independent random variables X and Y are given by fX (x) ¼ 2e2x u(x);
fY (y) ¼ ey u(y)
Hence the joint density function fXY(x,y) is fXY (x;y) ¼ 2eð2xþyÞ u(x)u(y) Given random variables Z ¼ max(X,Y ) and W ¼ min(X,Y ), we have to find the joint distribution FZW(z,w) and the marginal distributions FZ(z) and FW(w). The joint distribution FXY(x,y) can be obtained by integrating fX(x) and fY( y): FXY (x,y) ¼ 1 e2x ð1 ey Þu(x)u(y) ¼ 1 e2x ey þ e(2xþy) u(x)u(y)
13.2
Two Functions of Two Random Variables, Z 5 g(X,Y ), W 5 h(X,Y )
Using Eq. (13.2.10), we obtain 8 1 e2z ð1 ez Þ; z . 0, > < 2z w 2w z ð1 e Þ þ 1 e ð1 e Þ 1e FZW (z,w) ¼ > : 1 e2w ð1 ew Þ; z . 0, z . 0, 1 e2z ez þ e3z ; ¼ 2z z (2zþw) (zþ2w) 3w 1e e þe þe e ; z . 0,
225
w.z wz w.z wz
as the joint distribution function FZW(z,w). The marginal distribution FZ(z) is obtained by letting w ¼ z in the second equation of FZW(z,w) since w cannot exceed z. Thus FZ (z) ¼ 1 e2z ez þ e3z ,
z.0
which is the same as the first equation. To obtain FW(w), we let z tend to 1 in the second equation of FZW(z,w) and obtain FW (w) ¼ 1 e3w ,
w.0
Example 13.2.3 We are given Z ¼ X 2 þ Y 2 and W ¼ Y/X, where X and Y are zero mean independent Gaussian random variables with joint density function fXY (x,y) ¼
1 ½(x2 þy2 )=2s2 e 2ps2
The region Dxy is the set of all values of x,y such that x 2 þ y 2z and y/x w and is shown in Fig. 13.2.3. The joint distribution FZW(z,w) is found by integrating FZW(z,w) over the shaded region of the figure. ðð FZW (z,w) ¼
1 ½(x2 þy2 )=2s2 e dx dy 2 Dxy 2ps
FIGURE 13.2.3
226
Functions of Multiple Random Variables
pffiffi pffiffi pffiffi pffiffi Using polar coordinates, x ¼ z cos (u), y ¼ z sin (u), dx dy ¼ z d z du, we can rewrite the preceding equation as ð pffiz
ðu FZW (z,u) ¼ 2
(p=2) 0
1 (z=2s2 ) pffiffi pffiffi e z d z da 2ps2
with the factor 2 appearing since there are two shaded regions. This last equation can be simplified as follows: ð 1 p z 1 (z=2s2 ) pffiffi dz uþ z pffiffi e p 2 0 s2 2 z 1 u p p 2 þ ¼ 1 eðz=2s Þ ; z . 0, , u 2 p 2 2
FZW (z,u) ¼
Since w ¼ (y/w) ¼ tan(u), we can substitute u ¼ tan – 1(w) and obtain the marginal distributions FZ(z) and FW(w) as follows: 2
FZ (z) ¼ 1 e(z=2s ) ;
z . 0;
FW (w) ¼
1 tan1 (w) þ ; 1 , w , 1 2 p
The corresponding density functions are obtained by differentiation: fZ (z) ¼
1 (z=2s2 ) e ; z . 0; 2s2
fW (w) ¼
1 ; 1 , w , 1 p(1 þ w2 )
The functions FW(w) and fW(w) are Cauchy-distributed and are shown in Figures 13.2.4 and 13.2.5. The examples given above are some of the simple problems to solve with transformations of multiple functions of multiple random variables. In general, this method of solving for distribution functions of two functions of two random variables is more difficult. We now analyze alternate methods of finding the joint density functions.
FIGURE 13.2.4
13.3
Direct Determination of Joint Density fZW(z,w ) from fXY (x,y )
227
FIGURE 13.2.5
B 13.3 DIRECT DETERMINATION OF JOINT DENSITY fZW(z,w ) FROM fXY (x,y ) It is easier to obtain fZW(z,w) directly from the joint density function fXY(x,y) exactly as we did in the case of a single function of a single random variable. We are given the transformation Z ¼ g(X,Y ) and W ¼ h(X,Y ), and the inverse images g – 1(Z,W ) and h – 1(Z,W ) exist. However, corresponding to the differential parallelogram DDzw in the Z –W P plane, there may be several differential unconnected parallelograms DDxy ¼ dxkdyk, k ¼ 1, . . . , n in the X– Y plane as shown in Fig. 13.3.1. These differential regions are connected by the relation dz dw ¼ dx1 dy1 jJ(x1 ,y1 )j þ þ dxn dyn jJ(xn ,yn )j where jJ(x,y)j is the determinant of the Jacobian matrix J(x,y), defined by 2 3 @z(x,y) @z(x,y) 6 @x @y 7 7 J(x,y) ¼ 6 4 @w(x,y) @w(x,y) 5 @x @x
FIGURE 13.3.1
(13:3:1)
(13:3:2)
228
Functions of Multiple Random Variables
The joint density fZW(z,w) can be obtained from fXY(x,y) as follows: fZW (z,w)dz dw ¼ fXY (x1 ,y1 )dx1 dy1 þ þ fXY (xn ,yn )dxn dyn
(13:3:3)
Substitution of Eq. (13.3.1) in Eq. (13.3.3) results in fZW (z,w)dz dw ¼ ¼
fXY (x1 ,y1 )dz dw fXY (xn ,yn )dz dw þ þ jJ(x1 ,y1 )j jJ(xn ,yn )j n X fXY (xk ,yk )dz dw jJ(xk ,yk )j k¼1
(13:3:4)
n X fXY (xk ,yk ) jJ(x k ,yk )j k¼1
(13:3:5)
or fZW (z,w) ¼
From this derivation it is clear that certain restrictions have to be satisfied for g(x,y) and h(x,y) if we have to determine fZW(z,w) directly from fXY(x,y): 1. All the partial derivative of g(x,y) and h(x,y) must exist. 2. There cannot be any planar solutions to g(x,y) ¼ z and h(x,y) ¼ w. We can now formulate the methodology of finding fZW(z,w) from fXY(x,y). Steps for Direct Determination of fZW(z, w) 1. Check whether the restrictions on g(x,y) and h(x,y) are satisfied. 2. Find the n real solutions to the system of equations g(x,y) ¼ z;
h(x,y) ¼ w
and express them in terms of z and w. Let the n solutions be fxk, yk, k ¼ 1, . . . , ng. 3. Find the Jacobian determinant jJj for each xk,yk, and express this also in terms of z and w. 4. Find the density function fZW(z,w) from Eq. (13.3.5): fZW (z,w) ¼ Example 13.3.1
n X fXY (xk ,yk ) jJ(x k ,yk )j k¼1
The functions Z ¼ g(X,Y ) and W ¼ h(X,Y ) are defined by g(X,Y) ¼ X þ Y; h(X,Y) ¼ X Y
where X and Y are independent positive random variables with joint density fXY (x,y) given by 6e – (3xþ2y) . u(x)u( y). We have to find fZW(z,w). 1. All partials exist for g(x,y) ¼ x þ y and h(x,y) ¼ x y. 2. In the equations x þ y ¼ z and x y ¼ w, we note that z . 0 and w can have negative values. Solving for x and y results in one real solution: 1 1 1 zþw zw z x or x ¼ ; y¼ ¼ 1 w y 2 1 2 2 Since x and y are positive, we must have z . jwj and z . w. 3. The Jacobian determinant is jJj ¼ 2.
13.3
Direct Determination of Joint Density fZW(z,w ) from fXY (x,y )
229
FIGURE 13.3.2
4. The joint density function fZW(z,w) from Eq. (13.3.5) is given by h zþw 1 z wi þ2 fZW (z,w) ¼ 6 exp 3 ¼ 3e½(5zþw)=2 ; z . 0, z , w z 2 2 2 Finding the marginal densities fZ(z) and fW(w) are a little tricky because of finding the limits of integrations with respect to z and w. ðz 3e½(5zþw)=2 dw; z . 0, z , w z fZ (z) ¼ z
¼ 6 e2z e3z u(z) Function fZ(z) is shown in Fig. 13.3.2. Since w can take positive and negative values, we have the following integrals for fW(w) for the two regions w . 0 and w 0. In the region w . 0, we have ð1 fW (w) ¼ 3e½(5zþw)=2 dz; w . 0 w
6 ¼ e3w u(z) 5 and in the region w 0, we have ð1 fW (w) ¼
3e½(5zþw)=2 dz;
w0
w
¼
6 2w e u(w) 5
The function fW(w) is shown in Fig. 13.3.3. The distribution function FW(w) is obtained by integrating fW(w) and is given by 8 3 > < (e2w ) w0 FW (w) ¼ 5 > : 3 þ 2 (1 e3w ) w . 0 5 5 It is shown in Fig. 13.3.4.
230
Functions of Multiple Random Variables
FIGURE 13.3.3
Example 13.3.2 Two independent positive random variables X and Y are jointly exponentially distributed with density function fXY(x,y) ¼ 6e 2(3x þ 2y) u(x)u( y), exactly as in the previous example. Two other random variables are Z and W, given by, Z ¼ 2X=(X þ Y) and W ¼ X þ Y. We have to find the joint density fZW(z,w) and the marginal densities fZ(z) and fW(w). 1. All partials exist for g(x,y) ¼ 2x=(x þ y) and h(x,y) ¼ x þ y. 2. Solving for 2x=(x þ y) ¼ z and x þ y ¼ w, we have x ¼ zw=2 y ¼ ½1 (z=2)w. Since x and y are positive, w . 0 and 0 , z 2. 3. The Jacobian matrix is 2
2y 4 J ¼ (x þ y)2 1
3 2x (x þ y)2 5 1
and the determinant is j Jj ¼
2 2 ¼ xþy w
FIGURE 13.3.4
and
13.3
Direct Determination of Joint Density fZW(z,w ) from fXY (x,y )
231
4. The joint density function fZW(z,w) from Eq. (13.3.5) is given by nh zw w z io fZW (z,w) ¼ 6 exp 3 þ 2(1 )w ¼ 3wew½2þ(z=2) ; w . 0, 0 , z 2 2 2 2 Integrating over all w, we obtain fZ(z) ð1 fZ (z) ¼ 3wew½2þðz=2Þ dw ¼ 0
12 ; (z þ 4)2
0,z2
and the corresponding distribution function FZ(z) is 8 0: > > z0 < 4 : 0,z2 FZ (z) ¼ 3 1 > zþ4 > : 1: z.2 The functions fZ(z) and FZ(z) are shown in Fig. 13.3.5. Integrating over all z, we obtain fW(w) ð2 fW (w) ¼ 3w ew½2þðz=2Þ dz ¼ 6(e2w e3w );
w.0
0
and this result is the same one obtained in the previous example and shown in Fig. 13.3.2. Integrating fW(w), we obtain FW(w): FW (w) ¼ 1 þ 2e3w 3e2w ;
w.0
Example 13.3.3 We will find fZW(z,w) from fXY(x,y) for Example 13.2.3, where Z ¼ 2 2 2 X 2 þ Y 2 and W ¼ Y=X and the joint density fXY (x,y) ¼ (1=2ps2 )e½(x þy )=2s . 1. All partials exist for g(x,y) ¼ x2 þ y2 and h(x,y) ¼ y=x. 2. We solve x2 þ y2 ¼ z and y=x ¼ w. Substituting y ¼ wx in x2 þ y2 ¼ z, we obtain rffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffi z w2 z 2 2 and y ¼ + x 1 þ w ¼ z or x ¼ + 1 þ w2 1 þ w2
FIGURE 13.3.5
232
Functions of Multiple Random Variables
or
2 0rffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 3 2 z w z @ A; z . 0, 1 , w , 1 7 , 6 6 7 1 þ w2 1 þ w2 (x1 , y1 ) 6 7 ¼6 7 rffiffiffiffiffiffiffiffiffiffiffiffiffiffi! rffiffiffiffiffiffiffiffiffiffiffiffiffiffi (x2 , y2 ) 6 7 2 z w z 4 5 , ; z . 0, 1 , w , 1 2 2 1þw 1þw
3. The Jacobian matrix is given by
2
2x J¼4 y 2 x
3 2y 15 x
and the Jacobian determinant
y2 jJj ¼ 2 1 þ 2 ¼ 2(1 þ w2 ) x
4. The joint density fZW(z,w) is given by 1 1 (z=2s2 ) 1 ðz=2s2 Þ e þ e fZW (z,w) ¼ 2(1 þ w2 ) 2ps2 2ps2 or
1 1 (z=2s2 ) e ; fZW (z,w) ¼ p(1 þ w2 ) 2s2
z . 0, 1 , w 1
Integrating this equation with respect to w, the marginal density fZ(z) is ð1 1 dw 2 fZ (z) ¼ 2 e(z=2s ) 2 2s 1 p(1 þ w )
1 1 1 (w)
1 2 tan 2 ¼ 2 e1(z=2s ) ; z . 0 ¼ 2 e(z=2s )
2s p 2s 1 and by a similar integration with respect to z we obtain fW(w) as 1 fW (w) ¼ p(1 þ w2 ) which is a Cauchy density. These results correspond to the results obtained in Example 13.2.3. Example 13.3.4 Two independent zero mean Gaussian random variables X and Y has joint density function fXY(x,y) given by 1 (1=2)½(x2 þy2 Þ=s2 fXY (x,y) ¼ e 2ps2 Two other random variables Z and W are defined by 8 1 Y > > < tan pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X Z ¼ X2 þ Y 2; W ¼ > Y > : p þ tan1 X
if
X.0
if
X0
13.4
Solving Z 5 g(X,Y ) Using an Auxiliary Random Variable
233
The angle W has been defined in this manner so that it covers the region ((p=2), (3p=2) since tan1 (Y=X) covers only the region ((p=2),(p=2)]. We have to find the joint density fZW(z,w) and the marginal densities fZ(z) and fW(w). We apply the usual four steps to find a solution to this problem. pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1. All partials exist for g(x,y) ¼ x2 þ y2 and h(x,y) ¼ tan1 (y=x). pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2. We solve for x2 þ y2 ¼ z and tan1 (y=x) ¼ w for x . 0 and p þ tan1 (y=x) ¼ w ffiffiffiffiffiffiffiffiffiffiffiffiffiffi for x 0. We note that tan(w) ¼ y/x for both x 0 and x . 0. Squaring p x2 þ y2 ¼ z and substituting y=x ¼ tan (w), we can write y2 2 x 1 þ 2 ¼ z2 or x2 1 þ tan2 (w) ¼ z2 or x2 ¼ z2 cos2 (w) x However, for x . 0; (p=2) , w (p=2) and cos (w) . 0 and for x 0; (p=2) , w (3p=2) and cos(w) 0. Hence, the only solutions are x ¼ z cos(w) and y ¼ z sin(w) in the range (p=2) , w (3p=2). 3. The Jacobian matrix and its determinant are given by 2 3 x y pffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 6 x2 þ y2 1 1 x2 þ y2 7 7 J¼6 ¼ 4 y y 5 and jJj ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 z x þy x2 þ y2 x2 þ y2 4. The joint density fZW(z,w) is given by fZW (z,w) ¼
1 z (1=2)(z2 =s2 ) e ; z.0 2p s2
Integrating with respect to w in the range (p=2) , w (3p=2), we obtain z 2 2 fZ (z) ¼ 2 e(1=2)(z =s ) ; z . 0 s which is a Rayleigh distribution, and the angle w is uniformly distributed in (p=2) , w (3p=2), or fW (w) ¼
1 p 3p ; ,w 2p 2 2
and Z and W are also independent random variables.
B 13.4 SOLVING Z 5 g(X,Y ) USING AN AUXILIARY RANDOM VARIABLE The problem of finding the density function fZ(z) of a function of two random variables Z ¼ g(X,Y ) can be considerably simplified by converting to two functions of two random variables with the use of an auxiliary random variable W ¼ X or Y, provided g(x,y) satisfies the two restrictions of differentiability and no straight-line solutions. Example 13.4.1 We will now solve Example 13.1.10 using an auxiliary random variable. Here g(X,Y ) ¼ Z ¼ XY and we introduce an auxiliary random variable W ¼ h(X,Y ) ¼ X.
234
Functions of Multiple Random Variables
1. The restrictions on g(x,y) ¼ xy and h(x,y) ¼ x are satisfied. 2. Solving for xy ¼ z and x ¼ w, we obtain the real solution, x ¼ w and y ¼ z=w. 3. The Jacobian matrix and its absolute determinant are y x or kJk ¼ jxj ¼ jwj J¼ 1 0 4. The joint density fZW(z,w) is given by fZW (z,w) ¼
z 1 fXY w, jwj w
The function fZ(z) is obtained by integrating this equation with respect to w ð1 z 1 fXY w, dw FZ (z) ¼ w 1 jwj which is the same as Eq. (13.1.33). pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Example 13.4.2 This is the same as Example 13.1.7, where Z ¼ g(X,Y) ¼ X 2 þ Y 2 . We introduce the auxiliary random variable W ¼ h(X,Y ) ¼ X and proceed to solve it using the techniques of the previous section. The joint density fXY(x,y) is given by fXY (x,y) ¼
1 (1=2)½(x2 þy2 )=s2 e 2ps2
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1. The restrictions on g(x,y) ¼ x2 þ y2 and h(x,y) ¼ x are satisfied. pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2. Solving for x2 þ y2 ¼ z and x ¼ w,pwe obtain x2 þ y2 ¼ z2 or y2 p ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi z2 x2 , and ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 the two solutions are x1 ¼ w, y1 ¼ z w and x2 ¼ w, y2 ¼ z2 w2 with 2z , w z for real y1 and y2. 3. The Jacobian matrix and its absolute determinant are 2 3 x y pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffi z2 w2 y 2 2 2 2 4 5 ; x þy x þy J¼ or kJk ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ z x2 þ y2 1 0 4. The joint density function fZW(z,w) is given by z 2 (1=2)(z2 =s2 ) fZW (z,w) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi e ; z . 0, z , w z z2 w2 2ps2 Integrating with respect to the variable w, we obtain ðz z dw 2 2 1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; z . 0 fZ (z) ¼ 2 e(1=2)(z =s ) s p z z2 w2 Since the integrand is even, we can write this equation as ð z (1=2)(z2 =s2 ) 2 z dw pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; z . 0 fZ (z) ¼ 2 e s p 0 z2 w2 z z 2 2 2 p 2 2 ¼ 2 e(1=2)(z =s ) ; z . 0 ¼ 2 e(1=2)(z =s ) s p2 s which is the same as the result obtained in Eq. (13.1.22).
jwj z
13.4
Solving Z 5 g(X,Y ) Using an Auxiliary Random Variable
235
Example 13.4.3 This is the same as Example 13.1.8, where Z ¼ g(X,Y) ¼ X 2 þ Y 2 . We introduce the auxiliary random variable W ¼ h(X,Y ) ¼ X and proceed to solve it using the techniques of the previous section. The joint density fXY (x,y) is given by fXY (x,y) ¼
1 (1=2)½(x2 þy2 )=s2 e 2ps2
1. The restrictions on g(x,y) ¼ x2 þ y2 and h(x,y) ¼ x are satisfied. 2. Solving for x2pþffiffiffiffiffiffiffiffiffiffiffiffiffi y2 ¼ z and x ¼ w, we obtain from Y 2 ¼ z x2 , the two solutions pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffi pffiffi x1 ¼ w, y1 ¼ z w2 and x2 ¼ w, y2 ¼ z w2 , with z , w z for real y1 and y2. 3. The Jacobian matrix and its absolute determinant are pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffi 2x 2y or kJk ¼ 2y ¼ 2 z w2 ; z . 0, jwj z J¼ 1 0 4. The joint density function fZW(z,w) corresponding to the two solutions is given by, 1 2 (1=2)(z=s2 ) e ; fZW (z,w) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2ps 2 zw
pffiffi pffiffi z . 0, z , w z
Integrating with respect to the variable w, we obtain ð pffi 1 (1=2)(z=s2 ) z dw pffiffiffiffiffiffiffiffiffiffiffiffiffi ; z . 0 fZ (z) ¼ 2 e pffi 2 2s zp zw pffiffi pffiffi Substituting w ¼ z sin (u) and dw ¼ z cos (u)du, we obtain ð p pffiffi 2 1 z cos (u)du 2 1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; z . 0 fZ (z) ¼ 2 e(1=2)(z=s ) 2s p p2 z cos2 (u) ¼
1 (1=2)(z=s2 ) e ; z.0 2s2
which is the same result obtained in Eq. (13.1.24). Example 13.4.4 The random variable Z is given by Z ¼ (X þ Y )2, where X and Y are zero mean independent Gaussian random variables given by the joint density function fXY (x,y) ¼
1 (1=2)½(x2 þy2 )=s2 e 2ps2
We have to find the density function fZ(z) using auxiliary random variable W ¼ X. 1. The restrictions on g(x,y) ¼ (x þ y)2 and h(x,y) ¼ x are satisfied. pffiffi 2. Solving for (x þ y)2 ¼ z and x ¼ w, we obtain two solutions: x1 ¼ w, y1 ¼ z w pffiffi and x2 ¼ w, y2 ¼ z w. 3. The Jacobian matrix and its absolute determinant are pffiffi 2(x þ y) 2(x þ y) J¼ or kJk ¼ 2 z; 1 0
z.0
236
Functions of Multiple Random Variables
4. The joint density fZW(z,w) corresponding to the two solutions is 1 1 (1=2s2 )½w2 þ(pffizw)2 fZW (z,w) ¼ pffiffi e 2 z 2ps2 1 1 (1=2s2 )½w2 þ(pffizw)2 þ pffiffi e ; z.0 2 z 2ps2 We will now simplify this expression. Completing the squares in the first and the second terms on the righthand side of the equation, we obtain pffiffi 1 1 1 2 2 exp ½w þ ( z w) pffiffi 2 z 2ps2 2s2 ( " pffiffi2 #) 1 1 2 z z ¼ pffiffi exp 2 w þ 2 z 2ps2 2s 4 2 pffiffi 1 1 1 2 2 exp ½w þ ( z w) pffiffi 2 z 2ps2 2s2 ( " pffiffi2 #) 1 1 2 z z ¼ pffiffi exp 2 w þ þ 2 2 z 2ps2 2s 4 Hence the joint density fZW(z,w) is given by 1 1 (z=4s2 ) n (1=s2 )½w(pffiz=2)2 o fZW (z,w) ¼ pffiffi e e 2 z 2ps2 pffi (1=s2 )½wþ( z=2)2 þe ; z . 0, 1 , w , 1 We integrate this equation over all w to obtain fZ(z): ð o pffi 1 1 (z=4s2 ) 1 n (1=s2 )½w(pffiz=2)2 (1=s2 )½wþ( z=2)2 fZ (z) ¼ pffiffi e e þ e dw 2 z 2ps2 1 2
pffiffiffiffi 1 1 (z=4s2 ) pffiffiffiffi 1 e(z=4s ) pffiffiffiffi pffiffi ; z . 0 ¼ pffiffi e ½s p þ s p ¼ 2 z 2ps2 2s p z The distribution function FZ(z) is obtained by integrating fZ(z) over z: pffiffi ð z (z=4s2 ) 1 e z pffiffiffi dz ¼ erf FZ (z) ¼ pffiffiffiffi 2s 2s p 0 z Ðz 2 where erf(z) is defined by erf(z) ¼ p2ffiffipffi 0 ej dj and ð pffiffi 1 z ej erf( z) ¼ pffiffiffiffi pffiffiffi dj p 0 j Functions fZ(z) and FZ(z) are shown in Fig.13.4.1. Example 13.4.5 In this example X is a zero mean unit variance random variable given by the density function 1 2 fX (x) ¼ pffiffiffiffiffiffi e(x =2) 2p
13.4
Solving Z 5 g(X,Y ) Using an Auxiliary Random Variable
237
FIGURE 13.4.1
and Y is an independent chi-square random variable with n degrees of freedom whose density is given by Eq. (7.7.2): y(v=2)1 e(y=2) v u(y) 2v=2 G 2 pffiffiffiffiffiffiffiffi The density function of the random variable Z ¼ X Y=v is called the Student-t distribution (Section 7.9) and finds extensive application in statistical analyses. We will use an auxiliary variable W ¼ Y: fY (y) ¼
pffiffiffiffiffiffiffi 1. The restrictions on gðx;yÞ ¼ x= y=v and h(x,y) ¼ y are satisfied. pffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffi 2. Solving for x= y=v ¼ z and y ¼ w, we obtain the solution x ¼ z w=v and y ¼ w. 3. The Jacobian matrix and its absolute determinant are 2
3 1 x p ffiffiffiffiffiffiffi J ¼ 4 y=v 2v(y=v3=2 ) 5 0 1
1 1 or kJk ¼ pffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffiffiffi ; y=v w=v
w.0
4. The density function fZW(z,w) is given by pffiffiffiffiffiffiffiffi w(v=2)1 e(w=2) w=v 2 v u(w) fZW (z,w) ¼ pffiffiffiffiffiffi e(z w=v2) 2p 2v=2 G 2 w(v1)=2 2 e(w=2)½(z =vÞþ1 2 u(w) ¼ pffiffiffiffiffiffi v 2 pv G 2 The density function fZ(z) is found by integrating this equation over w, 1 fZ (z) ¼ pffiffiffiffiffiffi v 2 pv G 2
ð 1 (v1)=2 w 2 e(w=2)½(z =v)þ1 dw 0 2
238
Functions of Multiple Random Variables
This equation can be rewritten by substituting j ¼ ðw=2Þ½(z2 =nÞ þ 1, resulting in ð1 1 fZ (z) ¼ j(v1)=2 ej dj (vþ1)=2 pffiffiffiffiffiffi v z2 0 þ1 pv G 2 v The integral in this equation is a gamma function given by G½(v þ 1)=2. Thus vþ1 (vþ1)=2 G z2 2 fZ (z) ¼ pffiffiffiffiffiffi v 1 þ v pv G 2 which is a Student-t density [Eq. (7.9.2)].
B 13.5 MULTIPLE FUNCTIONS OF RANDOM VARIABLES We can now generalize the results of the previous section to n functions of n random variables. Let {X1 , . . . , Xn } be the n random variables defined by the joint density function fX1 , . . . , Xn (x1 , . . . , xn ). Let the n functions be Z1 ¼ g1 (X1 , . . . , Xn ), Z2 ¼ g2 (X1 , . . . , Xn ), . . . , Zn ¼ gn (X1 , . . . , Xn )
(13:5:1)
We assume that these functions satisfy the restrictions of existence of partial derivatives and no planar solutions. We now solve for g1 (x1 , . . . , xn ) ¼ z1 , g2 (x1 , . . . , xn ) ¼ z2 , . . . , gn (x1 , . . . , xn ) ¼ zn
(13:5:2)
Let the solutions be {x1 , . . . , xn } be expressed as functions of {z1 , . . . , zn }: x1 ¼ h1 (z1 , . . . , zn ), x2 ¼ h2 (z1 , . . . , zn ), . . . , xn ¼ hn (z1 , . . . , zn )
(13:5:3)
Let the Jacobian matrix be given by 2
@g1 6 @x1 J(x1 , . . . , xn ) ¼ 6 4 @gn @x1
... ...
3 @g1 @xn 7 7 @gn 5
(13:5:4)
@xn
and its determinant be expressed in terms of {z1 , . . . , zn }: kJk ¼ kJ(z1 , . . . , zn )k
(13:5:5)
The joint density function fZ1 , . . . , Zn (z1 , . . . , zn ) can now be given in terms of Eqs. (13.5.3) and (13.5.5): fZ1 , . . . , Zn (z1 , . . . , zn ) ¼
fX1 , . . . , Xn (x1 , . . . , xn ) kJ(z1 , . . . , zn )k ¼
fX1 , . . . , Xn ½h1 (z1 , . . . , zn )1 , . . . , hn (z1 , . . . , zn ) kJ(z1 , . . . , zn )k
(13:5:6)
For general functions {gk (x1 , . . . , xn , k ¼ 1, . . . , n}, {hk (z1 , . . . , zn , k ¼ 1, . . . , n} and kJ(z1 , . . . , zn )k are not that easy to solve. However, for linear functions
13.5
Multiple Functions of Random Variables
239
{gk (x1 , . . . , xn , k ¼ 1, . . . , n}, the solutions are not that difficult, as shown by the following example. Example 13.5.1 Three independent zero mean unit variance Gaussian random variables X, Y, and Z are given by the joint density function fXYZ (x,y,z) ¼
1 2 2 2 pffiffiffiffiffiffi e(1=2)(x þy þz ) 2p 2p
Three other random variables U, V, and W are given by 2 3 2 32 3 U 2 2 1 X 4 V 5 ¼ 4 5 3 3 54 Y 5 W 3 2 2 Z We have to find the joint density fUVW (u, v, w) and the marginal densities fU(u), fV(v), and fW(w). The solution to 2 3 2 32 3 u 2 2 1 x 4 v 5 ¼ 4 5 3 3 54 y 5 w 3 2 2 z is 2 3 2 x 0 4y5 ¼ 4 1 z 1
2 1 2
32 3 2 3 3 u 2v 3w 1 54 v 5 ¼ 4 u v þ w 5 4 w u 2v þ 4w
The Jacobian matrix and its determinant are given by 2 3 2 2 1 J ¼ 4 5 3 3 5; kJk ¼ 1 3 2 2 Hence, using Eq. (13.5.6), we obtain the joint density function fUVW(u, v, w) fUVW (u, v, w) ¼
2 2 2 1 1 pffiffiffiffiffiffi e(1=2)½(2v3w) þ(uvþw) þ(u2vþ4w) 1 2p 2p
1 2 2 2 pffiffiffiffiffiffi e(1=2)½(2u 9v 426w þ2uv30vw6wu) 2p 2p 8 2 319 0 u > > = < 1 1B 1 6 7C ½ u v w ½C v ¼ exp @ A 4 5 > > (2p)3=2 jCj1=2 ; : 2 w 2 3 9 19 12 where the matrix C ¼ 4 19 43 27 5and jCj ¼ 1. 12 27 17 ¼
The random variables X, Y, Z are independent, whereas the transformed random variables U, V, W are not independent. Their correlation coefficients can be calculated as follows: 19 rUV ¼ pffiffiffiffiffiffiffiffiffiffiffi ¼ 0:966, 9 43
27 rVW ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 0:999, 43 17
12 rWU ¼ pffiffiffiffiffiffiffiffiffiffiffi ¼ 0:970 17 9
240
Functions of Multiple Random Variables
By successively integrating with respect to (v,w), (w,u) and (u,v), we obtain the marginal density functions fU(u), fV(v), and fW(w) as ð1 ð1 1 2 2 2 fU (u) ¼ pffiffiffiffiffiffi e½(1=2)(2u þ9v 426w þ2uv30vw6wu) dv dw 2p 2p 1 1 1 2 ¼ pffiffiffiffiffiffi e½(1=2)(u =9) 3 2p ð1 ð1 1 2 2 2 fV (v) ¼ pffiffiffiffiffiffi e½(1=2)(2u þ9v þ26w þ2uv30vw6wu) dw du 2p 2p 1 1 1 2 ¼ pffiffiffiffiffiffi e½(1=2)(v =43) 43 2p ð1 ð1 1 2 2 2 fW (w) ¼ pffiffiffiffiffiffi e½(1=2)(2u þ9v 426w þ2uv30vw6wu) du dv 2p 2p 1 1 1 2 ¼ pffiffiffiffiffiffi e½(1=2)(w =17) 17 2p These results can be verified from the results of Example 13.1.5. Since var(ax) ¼ a 2 var(X ), we have var(U ) ¼ 22 þ 22 þ 12 ¼ 9, var(V ) ¼ 52 þ 32 þ 32 ¼ 43, and var(W ) ¼ 32 þ 22 þ 22 ¼ 17, which checks with the results above.
CHAPTER
14
Inequalities, Convergences, and Limit Theorems
B 14.1
DEGENERATE RANDOM VARIABLES
In Section 10.2 it was stated that if the variance is zero, then the random variable will no longer be random. We can state this concept more fully as follows. If E(X 2 a)2¼0, where a is a constant, then the random variable X equals a with probability 1. We shall show this result as follows. We construct a small hole of nonzero width 21 with 1 . 0 about the value a in the region of integration of x from 21 to 1 as shown in Fig. 14.1.1. We integrate (x2a)2 fX(x) from 21 to 1 except for the hole about a and write ð1 ð E(X a)2 ¼ (x a)2 fX (x)dx (x a)2 fX (x)dx (14:1:1) jxaj.1
1
By substituting the lowest value of 1 for jx –aj in Eq. (14.1.1), we can write ð ð 2 2 2 E(X a) 1 fX (x)dx ¼ 1 fX (x)dx ¼ 12 P(jX aj . 1) jxaj.1
jxaj.1
2
Since E(X a) ¼ 0 by assumption, we must have 12 P(jX aj . 1) ¼ 0: Since 1 is a nonzero constant, we must have P(jX aj . 1 ¼ 0: As a consequence P(jX aj ¼ 0) ¼ 1
and
X ¼ a with probability 1:
(14:1:2)
2
What this result conveys is that if E(X2a) ¼ 0, then almost all realizations of X will be equal to a constant a. The random variable X is called a degenerate random variable. This method of analysis will be useful in showing some of the inequalities that will follow. Example 14.1.1 If the variance s2X ¼ 0, then the random variable X ¼ mX almost certainly. Since variance of X equals E(X mX )2 ¼ 0, then, by Eq. (14.1.2) X ¼ mX with probability 1.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
241
242
Inequalities, Convergences, and Limit Theorems
FIGURE 14.1.1
B 14.2
CHEBYSHEV AND ALLIED INEQUALITIES
In many problems in communications we are interested in finding bounds on certain performance measures. The idea in using a bound is to substitute a simpler expression for something more complicated, thus gaining some insight into the problem at the expense of accuracy. This process usually requires a fair amount of experience with the results that are to be expected. One of the bounds that we are interested in is the tails probabilities of the type P(X . 1) or P(jXj . 1) (Fig. 14.2.1). Even if we cannot find these probabilities exactly, we should be able to find bounds on these quantities. These bounds are based on partial information about the random variable, such as mean and variance. Chebyshev Bound
The tails probability bound of the random variable X of the form P(jX –mXj , 1) is based on the knowledge of only the mean and the variance of X and is known as the Chebyshev inequality, given by P{jX mX j 1}
s2X 12
or
FIGURE 14.2.1
CB(1) ¼
s2X 12
(14:2:1)
14.2
Chebyshev and Allied Inequalities
243
where 1 is a positive constant .0 and CB(1) is the Chebyshev bound (this result can be shown using an analysis similar to that in the previous section): ð1 ð s2X ¼ (x mX )2 fX (x)dx (x mX )2 fX (x)dx (14:2:2) jxmX j.1
1
By substituting the lowest value of 1 in Eq. (14.2.2), we obtain ð s2X 12 fX (x)dx ¼ 12 P(jx mX j 1)
(14:2:3)
jxmX j.1
and hence Eq. (14.2.1). Geometric Derivation of the Chebyshev Bound We will now derive the Chebyshev bound from a different perspective. We will define a random variable Y ¼ X – mX. We will make use of the indicator functions defined in Eq. (12.5.7). Let A be a set on the real line such that A ¼ f –1, – 1g < f1, 1g. We define an indicator function IA as follows: 1 if Y [ A (14:2:4) IA ¼ 0 if Y A
The indicator function IA( y) is shown in Fig. 14.2.2. Taking the expected value of IA, we have E½IA ¼ 1 P(Y [ A) þ 0 P(Y A) ¼ P(Y [ A) ¼ P(jYj 1) ¼ P(jX mX j 1)
(14:2:5)
We will now bound IA by another random variable Y 2/12 as shown in Fig. 14.2.2. From the figure we can see that IA
Y 2 jX mX j2 ¼ 12 12
FIGURE 14.2.2
(14:2:6)
244
Inequalities, Convergences, and Limit Theorems
Taking expectation on both sides of Eq. (14.2.6), we have E½IA ¼ P(jX mX j 1)
E½Y 2 EjX mX j2 s2X ¼ ¼ 2 12 12 1
(14:2:7)
which is Chebyshev inequality. The bound is E½Y 2 =12 is called the Chebyshev bound. We will use this technique for deriving the Chernoff bound. Alternate Forms of the Chebyshev Bound By rearranging the terms in Eq. (14.2.1), we can rewrite Chebyshev’s inequality in terms of the center probability:
P{jX mX j , 1} 1
s2X 12
(14:2:8)
By substituting 1 ¼ nsX, we can write Eq. (14.2.1) as follows: P{jX mX j nsX }
1 n2
(14:2:9)
Thus, for any random variable X, the probability of a deviation from the mean mX of more than n standard deviations is 1/n 2. For example, in a standard Gaussian if n ¼ 3, 1=n2 ¼ 19 ¼ 0:1111. Chebyshev inequality tells us that the probability of deviation by more than 3 from 0 in a standard Gaussian is 0.1111. In actual cases this probability is 0.0027. We can also rewrite Eq. (14.2.5) analogous to Eq. (14.2.4) as P{jX mX j , nsX } 1
1 n2
(14:2:10)
Generalized Chebyshev Inequality
If f(x) is a convex function (Section 14.6) in the sense that its second derivative is positive for all x, then the inequality in Eq. (14.2.1) can be generalized to P{jXj . 1}
E½f(X) f(1)
(14:2:11)
This can be shown by using techniques similar to those used in demonstrating Chebyshev inequality, applying the fact that f(x) is convex: ð1 ð E½f(X) ¼ f(x)fX (x)dx f(x)fX (x)dx (14:2:12) 1
jxj.1
Using the least value for f(x), namely, f(1) in Eq. (14.2.12), we have ð E½f(X) f(1)fX (x)dx ¼ f(1)P{jXj . 1}
(14:2:9)
jxj.1
and hence Eq. (14.2.11). If f(X ) ¼ (X– mX)2 in Eq. (14.2.11), we get Chebyshev inequality. Example 14.2.1 We will apply the Chebyshev bounds to two different distributions, a Gaussian distribution and a Laplace distribution with zero means and unit variances.
14.2
Chebyshev and Allied Inequalities
245
FIGURE 14.2.3
They are given by 1 2 fN (x) ¼ pffiffiffiffiffiffi eðx =2Þ ; 2p
pffiffi 1 fL (x) ¼ pffiffiffi e 2jxj 2
and shown in Fig. 14.2.3. We will find the tails probabilities P(jXj 1) for both the distributions and the Chebyshev bound, which will be the same for both since they have the same means and variances. Applying Eq. (14.2.1), the Chebyshev bounds are P{jXj 1}
1 12
Thus the Chebyshev bound CB(1) ¼ 1/12. Since we know both the density functions, we can calculate the exact tails probabilities P (jXj 1), which are given by ð ð 1 1 x2 =2 1 1 x2 =2 1 e dx þ pffiffiffiffiffiffi e dx ¼ 1 erf pffiffiffi FN (1) ¼ pffiffiffiffiffiffi 2p 1 2p 1 2 ð 1 pffiffiffiffi ð 1 pffiffiffiffi p ffiffi 1 1 e 2x dx þ pffiffiffi e 2x dx ¼ e 2j1j FL (1) ¼ pffiffiffi 2 1 2 1 where
rffiffiffiffi ð 1 1 2 2 erf pffiffiffi ¼ ex =2 dx p 2 0
The one-sided tails probabilities FN (1) and FL(1) for the Gaussian density and the Laplace density, and the Chebyshev bound CB(1) are shown in Fig. 14.2.4 and tabulated in Table 14.2.1 for 1 ranging from 1.5 to 5. Conclusions
It is clear from Fig. 14.2.4 and Table 14.2.1 that the Chebyshev bound is a very loose one. As 1 increases, the bound becomes somewhat tighter (Fig. 14.2.4). Table 14.2.1 also shows that the Chebyshev bound is the same for both density functions, which have the same mean and variance. In fact, the Chebyshev bound is applicable to all density
246
Inequalities, Convergences, and Limit Theorems
FIGURE 14.2.4
TABLE 14.2.1 1 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.5 3.75 4 4.25 4.5 4.75 5
Normal FN(1)
Laplace FL(1)
Chebyshev CB(1) ¼ 1/12
0.133614 0.080118 0.0455 0.024449 0.012419 0.00596 0.0027 0.001154 0.000465 0.000177 0.000063 0.000021 0.000007 0.000002 0.000001
0.119873 0.084174 0.059106 0.041503 0.029143 0.020464 0.01437 0.01009 0.007085 0.004975 0.003493 0.002453 0.001723 0.00121 0.000849
0.444444 0.326531 0.25 0.197531 0.16 0.132231 0.111111 0.094675 0.081633 0.071111 0.0625 0.055363 0.049383 0.044321 0.04
functions having the same means and variances. It is useful as a theoretical tool to determine the bounds of the tails probabilities, where only means and variances are known about the density functions.
B 14.3
MARKOV INEQUALITY
There are several different versions of Markov inequality. The usual version is defined for a positive random variable X with a finite mean mX. It states that for a positive 1 . 0 m P(X 1) X (14:3:1) 1
14.3
Markov Inequality
247
This result can be shown by the usual techniques developed in the previous section ð1 ð1 ð1 mX ¼ xfX (x)dx xfX (x)dx 1 fX (x)dx ¼ 1P(X 1) (14:3:2) 0
1
1
and hence Eq. (14.3.1). In Eq. (14.3.1), the quantity mX =1 is called the Markov bound. Equation (14.3.1) can be slightly generalized to include both positive and negative random variables P(jX aj 1)
E(jX aj) 1
(14:3:3)
where a is an arbitrary constant and 1 . 0. Equation (14.3.3) can be further generalized to P(jX aj 1)
E(jX ajk ) 1k
(14:3:4)
where k is a positive integer. This inequality is called the Bienayme inequality. We will derive the value of a for which P(jX– aj 1) is a minimum in the equation P(jX aj 1)
E½jX aj2 12
(14:3:5)
for which we have to minimize E[jX – aj]2 with respect to a: E½jX aj2 ¼ E½X 2 2Xa þ a2 ¼ E½X 2 2aE½X þ a2
(14:3:6)
Differentiating Eq. (14.3.6) with respect to a and setting equal to 0, we have d {E½X 2 2aE½X þ a2 } ¼ 2a 2E½X ¼ 0 da from which we obtain a ¼ E[X ]. Thus we obtain Chebyshev inequality as follows: P(jX mX j 1)
E½jX mX j2 12
(14:2:1)
Example 14.3.1 A materials manager purchases 10,000 memory chips from a company where 3% of the chips are faulty. We have to find the probability that the number of good chips lies between 9675 and 9775. Without loss in generality, we will assume that the chips are independent. Let Xk be the zero – one random variable 1 if the kth chip is good Xk ¼ 0 if the kth chip is bad with the probability of Xk ¼ 1 is 0.97. Thus E[Xk] ¼ 1 0.97 þ 0 0.03 ¼ 0.97 and E[X2k ] ¼ 1 0.97 þ 0 0.03 ¼ 0.97. Hence var[Xk] ¼ 0.97 2 (0.97)2 ¼ 0.0291. If Y is the sum of the random variables fXk, k ¼ 1, . . . , 10,000), then the mean of Y is " # 10,000 10,000 X X Xk ¼ E½Xk ¼ 9700 E½Y ¼ mY ¼ E k¼1
k¼1
With the assumed independence of Xk, the variance of Y is " # 10,000 10,000 X X 2 Xk ¼ var½Xk ¼ 291 var½Y ¼ sY ¼ var k¼1
k¼1
248
Inequalities, Convergences, and Limit Theorems
With a ¼ ð9675 þ 9775Þ=2 ¼ 9725, 1 ¼ 50, and E½Y a2 given by E½Y a2 ¼ E½Y 2 2Ya þ a2 ¼ EY 2 2mY a þ a2 ¼ s2Y þ m2Y 2mY a þ a2 ¼ 291 þ 97002 2 9700 9725 þ 97252 ¼ 916 we can use Eq. (14.3.5) and write P(jY 9725j 50
916 ¼ 0:3664 502
What we want is the complementary event P(jY 9725j 50), and this is given by 1 2 0.3664 ¼ 0.6336. Thus, the probability that the number of good chips will lie between 9675 and 9775 is 63.36%. On the other hand, if we want the number of good chips to lie in the region 9700 + 50, namely, 9650 and 9750, then the probability using Chebyshev inequality is P(jY 9700j 50) ¼ 1 P(jY 9700j 50) ¼ 1
291 ¼ 0:8836 ¼ 88:36% 2500
Example 14.3.2 In a binomially distributed random variable b(k; n, p) ¼ nk pk qnk , we have n ¼ 50 and p ¼ 0.1. Using Markov inequality, we want to find the probability of X . 10 and compare it with the true value. The mean value is np ¼ 5 and 1 ¼ 10. Thus, from Eq. (14.3.1), we have the Markov bound P(X 10)
5 ¼ 0:5 10
and the true value is P(X 10) ¼
50 X
50! 0:1k 0:950k ¼ 0:02454 k!(50 k)! k¼10
This Markov bound is very loose! We will now find the Chebyshev bound from Eq. (14.2.7) with f(x) ¼ x 2 and compare the results. Equation (14.2.7) becomes P{jXj . 1} E½X 2 =12 and E[X 2] ¼ npq þ (np)2 ¼ 29.5. The Chebyshev bound is {X . 10} (29:5=100) ¼ 0:295, which is better than the Markov bound of 0.5, but nowhere close to the true value of 0.02454.
B 14.4
CHERNOFF BOUND
In determining the Chebyshev bound, the indicator function was bounded by a quadratic. In an effort to improve the tightness of the bound, we will use a positive exponential bound the indicator function as shown in Fig. 14.4.1. The expectation of the indicator function yields E½IA ¼ P(jYj 1) ¼ P(jX mX j 1) l(Y – 1)
(14:4:1)
, l . 0, 1 . 0, as shown in Fig. 14.4.1. From The indicator function is bounded by e the figure, IA e l(Y21) and taking expectations on both sides, we obtain E½IA ¼ P(Y 1) E½el(Y1)
14.4
Chernoff Bound
249
FIGURE 14.4.1
or P(Y 1) el1 E½elY ¼ el1 MY (l)
(14:4:2)
where jYj has been replaced with Y since we are considering only the positive bound and MY(l) is the moment generating function of Y. The bound given above will hold for any l . 0. However, we would like to choose l so that e 2l1E[e lY] is a minimum. Differentiating e 2l1E[e lY] with respect to l and setting it equal to zero, we obtain d d l(Y1) E½el(Y1) e ¼E dl dl l¼l0 l¼l0 or
E (Y 1)el0 (Y1) ¼ el0 1 E (Y 1)el0 Y ¼ 0
(14:4:3)
Rearranging terms in Eq. (14.4.3), we obtain an implicit relation for the minimum l0 in terms of the moment generating functions (MGFs):
1¼
E½Yel0 Y MY0 (l0 ) ¼ E½el0 Y MY (l0 )
(14:4:4)
The Chernoff bound is given by P(Y 1) el0 1 MY (l0 )
(14:4:5)
where l0 is given by Eq. (14.4.4). A similar result holds for the bound on the negative side. Example 14.4.1 We will find the bounds on tails probability for the standard Gaussian random variable Y using Chernoff’s formula and compare it to the Chebyshev bound found in Example 14.2.1. The MGF for any Gaussian random variable N is given by MN (t) ¼ etmþ(t MY (t) ¼ et
2
=2
2 2
s =2)
and for a standard Gaussian, Y N(0,1)
250
Inequalities, Convergences, and Limit Theorems
FIGURE 14.4.2
We will now find the optimum l from Eq. (14.4.4): 2
M 0 (l0 ) l0 e(l0 =2) ¼ 1¼ Y 2 MY (l0 ) e(l0 =2)
or l0 ¼ 1
The Chernoff bound from Eq. (14.4.5) is 2
2
P(Y 1) el0 1 el0 =2 ¼ e1 e1
2
=2
¼ e 1
2
=2
¼ ChG(1)
The Chernoff bound, ChG(1), the one-sided Ð 1 x2 =2 Chebyshev bound, CBO(1), and the one-sided normal distribution, FNo (1) ¼ p1ffiffiffiffi e dx are shown in Fig. 14.4.2 and in Table 14.4.1 2p e for 1 ¼ 1.5, . . . , 3. TABLE 14.4.1 1 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3
Normal FNo(1)
Chernoff ChG(1)
Chebyshev CBO(1) ¼ 1/212
0.066807 0.0548 0.044566 0.035931 0.028717 0.02275 0.017864 0.013903 0.010724 0.008197 0.00621 0.004661 0.003467 0.002555 0.001866 0.00135
0.324652 0.278037 0.235746 0.197899 0.164474 0.135335 0.110251 0.088922 0.071005 0.056135 0.043937 0.034047 0.026121 0.019841 0.014921 0.011109
0.222222 0.195312 0.17301 0.154321 0.138504 0.125 0.113379 0.103306 0.094518 0.086806 0.08 0.073964 0.068587 0.063776 0.059453 0.055556
14.5
Cauchy–Schwartz Inequality
251
From the figure and the table we see that for 1 . 2 the Chernoff bound is better than the Chebyshev bound. The crossover point occurs when 1 ¼ 2.075.
B 14.5
CAUCHY–SCHWARTZ INEQUALITY
An important inequality used in communication problems is the Cauchy – Schwartz inequality. Let g(x) and h(x) be two complex functions with finite norm defined by sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð 1
g(x)g (x)dx , 1
kg(x), g(x)l ¼ kg(x)k ¼ 1
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð
(14:5:1)
1
h(x)h (x)dx , 1
kh(x), h(x)l ¼ kh(x)k ¼ 1
The Cauchy–Schwartz inequality is given by sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð 1 ð1 ð1 2 g(x)h (x)dx jg(x)j dx jh(x)j2 dx 1
1
(14:5:2)
1
We can also state this inequality in terms of the inner product defined by ð1 kg(x), h(x)l ¼ g(x)h (x)dx ¼ kh(x), g(x)l
(14:5:3)
1
and the inequality becomes kg(x), h(x)l kg(x)k kh(x)k
(14:5:4)
If we define two real-valued functions, a(x) and b(x), as a(x) ¼ jg(x)j,
b(x) ¼ jh(x)j
then we have to show that sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð ð
ð1
1
1
1
a2 (x)dx
a(x)b(x)dx 1
b2 (x)dx
(14:5:5)
1
Let us represent a(x) and b(x) in a function space spanned by the basis vectors f1(x) and f2(x). These vectors have the following orthonormal property: ð1 ð1 ð1 2 2 f1 (x)dx ¼ f2 (x)dx ¼ 1; f1 (x)f2 (x)dx ¼ 0 (14:5:6) 1
1
1
Thus a(x) ¼ a1 f1 (x) þ a2 f2 (x);
b(x) ¼ b1 f1 (x) þ b2 f2 (x)
(14:5:7)
where a1,a2,b1,b2 are coordinates in the function space of ff1(x), f2(x)g and the vectors, a ¼ ½a1 ,a2 ; b ¼ ½b1 ,b2 as shown in Fig. 14.5.1.
252
Inequalities, Convergences, and Limit Theorems
FIGURE 14.5.1
We will find the inner product, ka(x), b(x)l ¼ can write ð1
Ð1 1
a(x)b(x)dx. From Eq. (14.5.7) we
ð1 ½a1 f1 (x) þ a2 f2 (x)½b1 f1 (x) þ b2 f2 (x)dx
a(x)b(x)dx ¼ 1
1
½using orthonormal properties of f1 (x) and f2 (x) ð1 ½a1 b1 f21 (x) þ a2 b2 f22 (x) dx ¼ a1 b1 þ a2 b2 ¼
(14:5:8)
1
From Fig. 14.5.1 we can write cos(u) ¼ cos(ua ub ) ¼ cos(ua ) cos(ub ) þ sin(ua ) sin(ub ) ¼
a1 b1 a2 b2 ab þ ¼ jaj jbj jaj jbj jajjbj
(14:5:9)
Since jcos(u)j 1, we have ja bj 1 and ja bj jajjbj (14:5:10) jajjbj ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Ð1 Ð1 Substituting for jaj ¼ 1 a2 (x)dx; jbj ¼ 1 b2 (x)dx in Eq. (14.5.10), we obtain Eq. (14.5.5) and hence Eq. (14.5.2). The equality is obtained when we substitute a ¼ Kb in Eq. (14.5.10) or a(x) ¼ Kb(x) and hence g(x) ¼ Kh(x). If g(X ) and h(X ) are real random variables, then we define the norms and the inner product given by Eqs. (14.5.1) and (14.5.3) as kg(X), g(X)l ¼ E½g2 (X) ¼ 2
ð1 1 ð1
kh(X), h(X)l ¼ E½h (X) ¼
g2 (x)f (x)dx , 1 (14:5:11) 2
h (x)f (x)dx , 1
1
ð1 g(x)h(x)f (x)dx ¼ kh(X), g(X)l
kg(X), h(X)l ¼ E½g(X)h(X) ¼ 1
(14:5:12)
14.5
Cauchy–Schwartz Inequality
253
FIGURE 14.5.2
and Cauchy–Schwartz inequality of Eq. (14.5.2) becomes ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð 1 ð1 ð1 2 2 g(x)h(x)f (x)dx g (x)f (x)dx h (x)f (x)dx 1
1
(14:5:13)
1
or pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi E½g(X)h(X) E½g2 (X)E½h2 (X) Example 14.5.1 A communication system is shown in Fig. 14.5.2, where s(t) is the input signal and n(t) is a random variable representing input noise, so(t) and no(t) are the output signal and output noise, H( f ) is the transfer function. gi(t) the input signal-to-noise ratio (SNR), and go(t) the output signal-to-noise ratio are given by gi (t) ¼
s2 (t) ; E½n2 (t)
go (t) ¼
s2o (t) E½n2o (t)
The output so(t) is given by the inverse Fourier transform of S( f )H( f ), or ð1 ð1 so (t) ¼ s(t)h(t t)dt ¼ S( f )H( f )e j2pf df 1
(14:5:14)
(14:5:15)
1
and the output noise is characterized by E½n2o (t) ¼
ð1
jH( f )j2 N( f )df
(14:5:16)
1
where N( f ) is the frequency-domain characterization of the input noise n(t). With these characterizations of Eqs. (14.5.15) and (14.5.16), the output SNR go(t) can be given by Ð 1 2 j2pf df s2o (t) 1 S( f )H( f )e ¼ Ð1 go (t) ¼ 2 E½n2o (t) 1 jH( f )j N( f )df
(14:5:17)
The Cauchy–Schwartz inequality of Eq. (14.5.2) is modified to suit the frequency-domain characterization as follows 2 ð 1 ð1 ð1 2 A( f )B ( f )df jA( f )j df jB( f )j2 df 1
1
1
with the equality holding for A( f ) ¼ KB ( f ). We substitute pffiffiffiffiffiffiffiffiffiffi A( f ) ¼ H( f ) N( f )
(14:5:18)
254
Inequalities, Convergences, and Limit Theorems
and S( f )e j2pft B( f ) ¼ pffiffiffiffiffiffiffiffiffiffi N( f )
(14:5:19)
A( f )B( f ) ¼ S( f )H( f )e j2pft
(14:5:20)
Thus
Applying Eqs. (14.5.19) and (14.5.20) in Eq. (14.5.18), the Cauchy–Schwartz inequality becomes 2 ð 1 ð 1 ð1 jS( f )j2 j2pft 2 df (14:5:21) S( f )H( f )e df jH( f )j N( f )df 1 1 1 N( f ) Substituting Eq. (14.5.21) in Eq. (14.5.17), the output SNR is given by ð1 ð1 jS( f )j2 2 df jH( f )j N( f )df 2 so (t) 1 1 N( f ) Ð ¼ g (t) o 1 2 E½n2o (t) 1 jH( f )j N( f )df or ð1 go (t)
jS( f )j2 df 1 N( f )
(14:5:22)
The equality will hold for A( f ) ¼ KB ( f ), or pffiffiffiffiffiffiffiffiffiffi S ( f )ej2pfT H( f ) N( f ) ¼ K pffiffiffiffiffiffiffiffiffiffi N( f )
or Hopt ( f ) ¼ K
S ( f )ej2pfT jN( f )j
(14:5:23)
where we have substituted T the time of observation for t. The maximum output SNR is ð1 jS( f )j2 go, max df (14:5:24) 1 jN( f )j
B 14.6
JENSEN’S INEQUALITY
Convex Functions
Before we enunciate Jensen’s inequality, we want to make clear the idea of a convex function. We are interested in a convex function because convexity guarantees a unique minimum over the interval of convexity. A function h(x) is convex in the interval (a, b) if for every x1 belonging to the interval (a,b), there exists a constant m such that the following inequality is satisfied for all x: h(x) h(x1 ) þ m(x x1 )
(14:6:1)
As seen in Fig. 14.6.1, h(x1 ) þ m(x x1 ) is the equation to a straight line passing tangentially to the point h(x1) and m is the slope of the straight line at the point x1. It is clear from the figure that the inequality is satisfied geometrically. For the sake of completeness, we will give a more formal definition of convexity.
14.6
Jensen’s Inequality
255
FIGURE 14.6.1
A function h(x) is convex over an interval (a,b), if every x1 and x2 belonging to (a,b) and for 0 l 1, the following equation holds. h½lx1 þ (1 l)x2 lh(x1 ) þ (1 l)h(x2 )
(14:6:2)
If this equation is a strict inequality, then h(x) is strictly convex. Geometrically, this equation conveys that if the function h(x) lies below the line segment connecting two points in the interval (a,b), then it is convex. It can also be shown that it is necessary and sufficient that the second derivative d2 h(x)=dx2 0 for all x in the interval (a,b), for Eq. (14.6.2) to be satisfied, in which case h(x) is convex. Example 14.6.1 is nonnegative:
The following functions h(x) are convex because the second derivative
1. x2 , since h00 (x) ¼ 2, positive for all x 2. jxj, since h00 (x) ¼ 0, nonnegative for all x 3. ex , since h00 (x) ¼ ex , positive for all x 4. ln(x), for 0 , x , 1, since h00 (x) ¼ 1=x2 , positive in the range of x 5. x ln(x), for 0 , x , 1, since h00 (x) ¼ 1=x, positive in the range of x Jensen’s Inequality
Jensen’s inequality states that if h(x) is convex and if X is a random variable with EjXj , 1, then E½h(X) h E½X (14:6:3) Substituting E[X ] ¼ x1 in the convexity [Eq. (14.6.1)], we obtain h(x) h E½X þ m x E½X Taking expectations on both sides of Eq. (14.6.4) gives E½h(X) h E½X þ m E½X E½X ¼0 and hence Jensen’s inequality [Eq. (14.6.3)].
(14:6:4)
256
Inequalities, Convergences, and Limit Theorems
Example 14.6.2 Since E[X 2n]¼ E[(X n)2] is a convex function from Example 14.6.1, it follows from Jensen’s inequality that 2 E½X 2n E½X n (14:6:5) and as a consequence 2 E½X 2 E½X
(14:6:6)
which is a restatement of the fact that the variance of a random variable is nonnegative.
B 14.7
CONVERGENCE CONCEPTS
We saw in Section 14.1 that if E(X a)2 ¼ 0, then X ¼ a with probability 1 or X converges to a with probability 1. There are a few other convergence concepts, and we shall make these a little clearer. A machinist wants to make piston rings of diameter a inches to a tolerance limit of +1. Let us assume that the variability of the measurement is s2X. According to the Chebyshev inequality of Eq. (14.2.8), we have P{jX aj , 1} 1
s2X 12
(14:7:1)
If sX is very much smaller than 1, then the observed random variable X is between a 2 1 and a þ 1 is almost certain, and one measurement of X is sufficient. However, if sX is not sufficiently small compared to 1, then the estimate a will not give results of sufficient accuracy. To improve the accuracy of the estimate a, we take n measurements corresponding to n random variables fXi, i ¼ 1, . . . , ng with mean a and noise random variables Wi given by Xi ¼ a þ W i ,
i ¼ 1, . . . , n
where we will assume that the random variables Wi are independent with zero mean and variance s2. We will represent the average of these n random variables by X 1 þ X2 þ þ Xn b X¼ (14:7:2) n where the mean of b X is a and the variance is ns2X and the corresponding Chebyshev inequality is given by P{jX aj , 1} 1
s2X n12
(14:7:3)
We can see from Eq. (14.7.3) that as the number of samples n becomes very large, the probability that the jX aj is less than 1 becomes almost certain even though s2X may not be small compared to 1. Pointwise Convergence
A discrete sequence of random variables {X1 , X2 , . . . , Xn , . . .}
(14:7:4)
converges to a limiting random variable X if and only if for any 1 . 0, however small, we can find a number n0 such that jXn (j) X(j)j , 1
(14:7:5)
14.7
Convergence Concepts
257
for every n . n0 and every j. This type of convergence is also called pointwise or everywhere convergent. This may be all right for a real variable, but it is highly restrictive for a random variable. It is possible that the sequence fXng may converge for j points belonging to a subset of the sample space S. Thus, convergences using probability measures are more useful than this type of convergence. Almost Sure Convergence (a.s.)
A sequence of random variables fXng converges almost surely (a.s.), or almost everywhere (a.e.), or strongly to the random variable X, if for every j point in the sample space S satisfies the following criterion: lim jXn (j) X(j)j ! 0 with probability 1
n!1
(14:7:6)
The criterion can also be written as P(Xn ! X) ¼ 1
as
n ! 1
(14:7:7)
In Eq. (14.7.7) the quantity Xn ! X is to be interpreted as the set of all outcomes j such that the number Xn(j) tends to the number X(j). In some cases the limit X may not be known a priori. In this case the Cauchy criterion of convergence comes handy to define mutual convergence. Cauchy Convergence Criterion
A sequence of random variables fXng on the probability space {S, F, P} is convergent almost surely if the set of points j for which the real sequence {Xn (j)} is convergent with probability 1. Or, in other words P{j [ S : Xnþm (j) Xn (j) ! 0}
as
n ! 1
(14:7:8)
for every m . 0. Using this criterion, the sequence fXng converges mutually almost surely if P{Xnþm Xn } ! 0
as
n ! 1
(14:7:9)
Convergence in Probability (p)
A sequence of random variables fXng converges in probability or weakly converges to the random variable X if for every 1 . 0, however small lim P{jXn Xj 1} ¼ 0
(14:7:10)
lim P{jXn Xj 1} ¼ 1
(14:7:11)
n!1
or equivalently n!1
Using the Cauchy criterion, the sequence fXng converges mutually in probability to X if lim P{jXnþm Xn j 1} ! 0
n!1
(14:7:12)
Convergence in probability is widely used in probability theory. In particular, it is used to determine the weak law of large numbers and the consistency of estimators.
258
Inequalities, Convergences, and Limit Theorems
Convergence in Mean Square (m.s.)
A sequence of random variables fXng converges in the mean square or limit in the mean to the random variable X if lim E{jXnþm Xj2 } ! 0
n!1
(14:7:13)
This is also called quadratic mean convergence. Mutual convergence in the mean square is defined by lim E{jXnþm Xn j2 } ! 0
n!1
(14:7:14)
for all m . 0. Convergence in Distribution
The widest form of convergence is the convergence in distribution. If we denote the distribution functions of Xn and X by Fn(x) and F(x), respectively, then the sequence fXng converges in distribution to the random variable X if lim Fn (x) ! F(x)
n!1
(14:7:15)
for every continuity point x of F(x). However, the sequence fXn(j)g need not converge for any j. Properties of Convergences 1. Almost-sure convergence implies convergence in probability. The converse is not true. 2. Mean-square convergence also implies convergence in probability. Again the converse is not true. 3. If fXng converges in probability, then there exists a subsequence fXnkg of fXng that converges almost surely. 4. Convergences almost surely, probability, and mean square all imply convergence in distribution. Of course, the converse is not true. All the four convergences are shown schematically in Fig. 14.7.1.
FIGURE 14.7.1
14.8
B 14.8
Limit Theorems
259
LIMIT THEOREMS
Weak Law of Large Numbers (WLLN)
Armed with the convergence concepts, we will now explore some limit theorems. Let fXng be a sequence of independent identically distributed random variables with finite means m and finite variances s2. Let Sn be the sum of the n random variables given by Sn ¼ X 1 þ X2 þ þ Xn
(14:8:1)
Let the random variable Zn, called the sample mean, be defined by X1 þ X2 þ þ X n n
Zn ¼
Clearly the expected value of Zn is X1 þ X2 þ þ Xn nm ¼m E½Zn ¼ E ¼ n n and because of the independence of the sequence fXng, the variance of Zn is X 1 þ X2 þ þ Xn ns2 s2 var½Zn ¼ var ¼ 2 ¼ n n n
(14:8:2)
(14:8:3)
(14:8:4)
The weak law of large numbers states that for every 1 . 0 lim P½jZn mj . 1 ! 0
n!1
(14:8:5)
To show this result, we apply Chebyshev’s inequality [Eq. (14.2.1)] P½jZn mj . 1
s2 n12
(14:8:6)
and since 1 . 0, the limit of Eq. (14.8.6) as n tends to 1 is indeed 0. In other words, the probability of the sample mean Zn for a specific n of a sequence fXng of random variables deviating from the true mean m by an arbitrary 1 . 0 becomes vanishingly small as n tends to 1. Or the sample mean Zn converges in probability to the true mean. Example 14.8.1 In the design of a gyroscope for a navigation system we want the probability of the sample drift rate Zn calculated for n production gyros differing from the unknown true drift rate m by more than 0.1 be less than 2%. We want to find the number n of the gyros needed to establish this criterion expressed in terms of the unknown variance s2. Applying Chebyshev inequality given by Eq. (14.2.8), we can write 1 s2 100 . 0:98 P jZn mj , 1 10 n From 1 ð100s2 =nÞ . 0:98 we have 1 0:98 n
100s2 n 100s2 ¼ 5000s2 0:02
If the quality control is very good, s will be small, in which case the number n will be small. However, since we are using Chebyshev inequality that gives very loose bounds,
260
Inequalities, Convergences, and Limit Theorems
we expect n to be overly conservative. We will revisit this example after discussing the central limit theorem in the next section. Example 14.8.2 We will assume that there are a sequence of independent events fAig that will be denoted by the sequence of indicator functions IAi (j), given by
1 if j [ Ai IAi (j) ¼ 0 if j [ Ai Zn ¼
n 1X IA n i¼1 i
We will assume that P(Ai ) ¼ p and P(Ai ) ¼ 1 p for i ¼ 1, . . . , n. Thus E½IAi ¼ p
and
E½IA2i ¼ p
and
E½Zn ¼ p
and
var½Zn ¼
p(1 p) n
var½IAi ¼ p(1 p), i ¼ 1, . . . , n
We can now apply WLLN and write P{jZn pj , 1} 1
p(1 p) n12
Assuming p ¼ 0.4, we will estimate p within 0.1. If n ¼ 500, then this equation becomes P{jZn pj , 0:1} 1
0:4 0:6 8 117 ¼1 ¼ 500 0:01 125 125
What this equation tells us is that if we perform 500 trials, then 468 of these will result in an error of fZn 2 pg of less than 0.1. Strong Law of Large Numbers
The weak law of large numbers given by Eq. (14.8.5) states that the sample mean Zn converges in probability to the true mean m as n tends to 1. The strong law of large numbers states that this convergence is not only in probability but also with probability 1, which is almost surely (a.s.). Or n o (14:8:7) P lim (Zn m) ¼ 0 ¼ 1 (a.s.) n!1
Unlike the weak law of large numbers, which is easy to show with Chebyshev’s inequality, the strong law of large numbers is difficult to show and will not be attempted in this book. Even though Eqs. (14.8.5) and (14.8.7) look similar, they are dramatically different. Equation (14.8.7) states that the sequence of the sample means Zn for every n will tend to the true mean m and will stay close to it. The strong law gives us confidence that sample means calculated from a number of realizations will always tend to the true mean as the number increases and isolated discrepancies become unimportant. Central Limit Theorem (CLT)
Perhaps the most important theorem in statistics is the central limit theorem, which in its most general form states that if we sum a sufficient number of random variables, the resulting probability is a Gaussian. Let fXng be a sequence of n independent
14.8
Limit Theorems
261
identically distributed random variables with mean mX and variance s2X. Their sum Sn is given by Sn ¼ X 1 þ X2 þ þ Xn
(14:8:8)
The mean m and the variance s2 of Sn are E½Sn ¼ m ¼ nmX var½Sn ¼ s2 ¼ ns2X
(14:8:9)
We will now define a normalized random variable Zn with mean 0 and variance 1 as follows: Zn ¼
Sn m Sn nmX pffiffiffi ¼ s sX n
(14:8:10)
The central limit theorem states that as n tends to 1, Zn tends to a standard normal distribution: ðz 1 2 pffiffiffiffiffiffi e(x =2) dx lim P(Zn z) ¼ (14:8:11) n!1 2p 1 We can show this result heuristically by using moment generating functions (MGFs) in Zn ¼
n Sn nmX 1 X pffiffiffi ¼ pffiffiffi (Xi mX ) sX n i¼1 sX n
(14:8:12)
Taking MGF on both sides, we obtain pffiffi Pn (t=sX n) (X m ) tZn i X i¼1 MZn (t) ¼ E(e ) ¼ E e " ¼E
n Y
e
pffiffi (t=sX n)(Xi mX )
#
i¼1
( )n n Y pffiffi pffiffi ¼ E e(t=sX n)(Xi mX ) ¼ E e(t=sX n)(XmX )
(14:8:13)
i¼1
In Eq. (14.8.13) we have used the independence property of fXng and X has the same pdf as Xn. We can now expand the last exponential in the above equation and write h i pffiffi 1 t2 (t=sX n)(XmX ) 2 E e (X mX ) þ hn (X; t) ¼ E 1 þ pffiffiffi (X mX ) þ sX n 2ns2X ¼1þ0þ
t2 þ E½hn (X; t) 2n
where hn(X; t) represents the higher-order terms. Hence
n t2 MZn (t) ¼ 1 þ 0 þ þ E½hn (X; t) 2n
n t2 2 lim MZn (t) ¼ 1 þ ¼ et =2 n!1 2n
(14:8:14)
262
Inequalities, Convergences, and Limit Theorems 2
Since E[hn(X; t)] is of the order of 1/n 2, it is zero in the limit as n tends to 1. The term et =2 is the MGF of a standard Gaussian, and we have shown the central limit theorem. The number n is usually assumed to be 30 for CLT to hold, but as we shall see in the following example, it holds for n much less than 30. Example 14.8.3 Random variables Xi, i ¼ 1,2,3,4 are independent and identically distributed with a uniform distribution in the interval (0,1] with fXi(x) ¼ u(x) 2 u(x 2 1). 1 The mean values are mXi ¼ 12 and the variances are sXi2 ¼ 12 . We form the random variables Y1 ¼ X1 , Y2 ¼ X1 þ X2 , Y3 ¼ X1 þ X2 þ X3 , Y4 ¼ X1 þ X2 þ X3 þ X4 , and their density functions are fY1 (y) ¼ fX1 (y), fY2 (y) ¼ fX1 (y) fX2 (y), fY3 (y) ¼ fY2 (y) fX1 (y), fY4 (y) ¼ fY3 (y) fX1 (y). These densities are given by
fY1 ( y) ¼
1 0
if 0 , y 1 otherwise
8 if 0 , y 1 > : 0 otherwise
8 2 y > > > if 0 , y 1 > > 2 > > > 3 < 2 y þ 3y if 1 , y 2 fY3 ( y) ¼ 2 > > y2 > 9 > > if 2 , y 3 3y þ > > 2 > :2 0 otherwise 8 3 y > > if 0 , y 1 > > 6 > > > > y3 2 > > > þ 2y2 2y þ if 1 , y 2 > < 2 3 3 fY2 ( y) ¼ y 22 > if 2 , y 3 4y2 þ 10y > > 3 >2 > > > y3 32 > > > þ 2y2 8y þ if 3 , y 4 > > 6 3 : 0 otherwise These densities are compared to the corresponding normal densities given by 1 12 fNi ( y) N(mNi , s2Ni ) ¼ N i , i , i ¼ 1,2,3,4 2 12 or 2 1 fN1 ( y) ¼ rffiffiffiffi e½x(1=2) =(1=6) p 6
2 1 fN2 ( y) ¼ rffiffiffiffi e(x1) =(1=3) p 3
2 1 fN3 ( y) ¼ rffiffiffiffi e½x(3=2) =(1=2) p 2
2 1 fN4 ( y) ¼ rffiffiffiffiffiffi e(x2) =(2=3) 2p 3
These densities are shown in all four panels of Fig. 14.8.1. We see that the approximation progressively improves as n increases from 1 to 4, and it is very good when n ¼ 4 (Fig. 14.8.1d).
14.8
Limit Theorems
263
FIGURE 14.8.1
Example 14.8.4 We will revisit Example 14.8.1 in light of the CLT and determine whether we can get a better bound on the number n of gyros needed. In order to use the Gaussian tables, we have to convert the random variable Zn into the standard Gaussian form by substituting pffiffiffi n(Zn m) Zn m Wn ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ s var(Zn ) 1 0:98 and arrive at We can substitute Wn in P½jZn mj , 10 pffiffiffi n sjWn j 1 P pffiffiffi , 0:98 0:98 or P jWn j , 10s 10 n
Referring pffiffiffi to Gaussian tables, we have the value for a probability of 0.98 is x ¼ 2.3263. Thus, n=10s 2:3263 or n 2:32632 100s2 ¼ 541:17s2 and n 542 s2. This is a much better number than the 5000s2 that we obtained in Example 14.8.1.
CHAPTER
15
Computer Methods for Generating Random Variates
If we have an actual probabilistic system, we can obtain the pdf by analyzing the data from the system. On the other hand, if we are designing a system such as transmission of packets through a communication channel, we must get some idea as to how the system will behave before it is actually implemented. We resort to simulation of the system on a computer. The probability distributions may be known a priori, and we have to generate random data for the given probability distributions. We call such generated values random variates. It is not feasible to develop techniques for generating random variates for every possible distribution function. As it turns out, generating a uniform distribution in the interval (0,1] is fast on a computer. Starting with the uniform distribution, we can generate random variates for any given distribution function by a number of techniques that will be discussed.
B 15.1
UNIFORM-DISTRIBUTION RANDOM VARIATES
Almost every computer will have a uniform number generator built into the system using some form of the modulus function. One such generator uses the linear congruential or power residue method that produces a sequence of integers fxi, i ¼ 0, . . . , m 2 2g. It is given by the recursive relationship xiþ1 ¼ (a xi þ c)mod m
(15:1:1)
where a is a constant multiplier, c is the increment, and m is the modulus function. The initial value x0 is called the “seed.” Equation (15.1.1) can be recursively solved to yield n a 1 x n ¼ an x 0 þ c mod m (15:1:2) a1 All these parameters affect the mean, variance, and cycle length of the sequences. It is called a mixed congruential generator if c = 0 and multiplicative congruential generator if c ¼ 0. The sequence repeats after xm22 and the period of repetition is less than or equal to m 2 1 depending on the parameters.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
264
15.1
265
Uniform-Distribution Random Variates
FIGURE 15.1.1
We will form two sequences with
Example 15.1.1
1. fxig : x0 ¼ 1, a ¼ 7, c ¼ 0, m ¼ 13 2. fyig : y0 ¼ 1, a ¼ 7, c ¼ 3, m ¼ 13 Using the recursive relationship of Eq. (15.1.1), these two sequences fxig and fyig are shown in Fig. 15.1.1 and Table 15.1.1. As we can see from Fig. 15.1.1 and Table 15.1.1, the repetition period is 13 2 1 ¼ 12. Also changing c from 0 to 3 has radically changed the sequence. We can conclude that almost all random-number generators are not truly random but pseudorandom sequences that have the appearance in the statistical sense of being random. Thus, when we refer to random numbers we really mean pseudorandom sequences where the period is very high. A random-number generator must have the following properties: 1. The numbers generated must have good statistical properties. They must be independent and identically distributed. In other words, the values may not be correlated. 2. The repetition period must be long for any simulation to be useful. 3. Random numbers must be repeatable. For each specified x0, a, c, and m, the generator must produce the same sequence of numbers. 4. Many simulations will require thousands of random numbers, and hence the generation of these numbers must be fast and thus must also have computational efficiency. 5. It must be easy to generate separate sequences of random numbers.
TABLE 15.1.1 i
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
2
1
7
10
5
9
1
10
8
7
Series 1: x0 ¼ 1, a ¼ 7, c ¼ 0, m ¼ 13 xi
1
7
10
5
9
11
12
6
3
8
4
Series 2: x0 ¼ 1, a ¼ 7, c ¼ 3, m ¼ 13 yi
1
10
8
7
0
3
11
2
4
5
12
266
Computer Methods for Generating Random Variates
The linear congruential generator may satisfy all these conditions provided a and m are carefully chosen. Good statistical properties have been obtained with a ¼ 75 ¼ 16,807 and m ¼ 231 2 1 ¼ 2,147,483,647. Under these conditions the period of the sequence is 231 2 2 ¼ 2,147,483,646. Sometimes a ¼ 742,938,285 and m ¼ 248 are also used.
B 15.2
HISTOGRAMS
Having generated random variates, we have to see how well they approximate the desired probability density. From the definition of the density function fX (x), we have fX (x)
P(X x þ Dx) P(X x) Dx
or fX (x)Dx P(x , X x þ Dx)
(15:2:1)
which gives the value of the probability mass at the point x and width Dx. Hence with the generated random variates fxig we will be able to construct a histogram that will approximate the given mass function fX (x)Dx. The purpose of the histogram is to construct the model of the desired distribution from the generated data. The usual form of the histogram is to divide the range of data points N into equal-size bins or subintervals Dx. Or the necessary number of bins K needed will be given by Number of bins K ¼
range of data N bin width Dx
(15:2:2)
Next, the number of data points fni, i ¼ 1, . . . , Kg falling into each bin is counted. The vertical axis (ordinate, y) represents the counts in each bin, and the horizontal axis (abscissa, x ) represents K . Dx, the product of the number of bins K and the bin interval Dx. If fX (x) is finite, then the product K . Dx must cover the entire range of x. If it is infinite or semiinfinite, then the product can cover a reasonable range. Finally, the histogram must be normalized so that it can be compared to the desired density function. The normalization proceeds as follows. The normalized count is the count in each bin fni, i ¼ 1, . . . , Kg, divided by the total number N of observations multiplied by the bin width Dx so that the area under the histogram equals one. This type of normalization results in a histogram that is similar to the probability density function. Even though this may be less intuitive, it is the one to be used if the histogram is to model the desired probability density function. We will now model a uniform distribution U(0,1) from the random variates generated by the linear congruential generator of a computer. For this model, N ¼ 10,000 points were generated and K ¼ 100 bins were chosen with each bin interval Dx ¼ 0.01. Note that K . Dx ¼ 1 covers the x axis of the U(0,1) distribution. The histogram subroutine is called that places the counts fni, i ¼ 1, . . . , Kg in each of the K bins. The number of points falling in the first 10 bins is shown in Table 15.2.1. The expected number in each bin is 100. The resulting histogram is divided by N . Dx ¼ 10,000 0.01 ¼ 100 to yield the model for U(0,1) distribution. The normalized histogram is shown in Fig. 15.2.1. Superimposed over the histogram is the actual uniform distribution. By “eyeballing” the two plots in Fig. 15.2.1, we see that the modeling is indeed good. More formally, we can also test the goodness of fit by a number of mathematical tests, and we will use the chisquare test to ascertain whether the histogram fits the true pdf sufficiently well. We will now enumerate the procedure for the chi-square test as applied to this case.
15.2 Histograms
267
TABLE 15.2.1 Bins Count Expected
0
1
2
3
4
5
6
7
8
9
102 100
100 100
109 100
100 100
117 100
107 100
86 100
93 100
86 100
81 100
Chi-Square Test 1. From the histogram find the number of observations ni in each of the i ¼ 1, . . . , K bins. 2. From the probability density function fX (x) calculate the occupancy mi ¼ fX(xi)NDx in each of the i ¼ 1, . . . , K bins. 3. Calculate the deviation Di from Di ¼ ni mi ; i ¼ 0; . . . ; K 1: PK1 4. Form the chi-square statistic from x ¼ i¼0 (Di =mi )2 . This will have K22 degrees of freedom. 5. Set the probability level pg or the significance level a ¼ 1 2 pg. Usually pg is set at 95% or 99% or a is set at 5% or 1%. The higher the significance level, the tighter will be the bounds. 6. From the chi-square tables find the threshold value ta for the given significance level and K22 degrees of freedom. 7. If the x value is less than ta, the hypothesis that the fit is good is accepted. Otherwise it is rejected. We can use the x2 test to determine the goodness of the fit to the uniform distribution that has been achieved. The x value is found to be x ¼ 91.39 at the 5% significance level. The corresponding ta value for 100 2 2 ¼ 98 degrees of freedom at the 5% significance level is obtained from Table 15.2.2 as ta ¼ 122.10773. The chi-square density and distribution for 98 degrees of freedom is shown in Figs. 15.2.2 and 15.2.3 with the probability and significance levels. The hypothesis that the histogram is a good fit to the true normal distribution is accepted since 91.39 , 122.10773.
FIGURE 15.2.1
268
Computer Methods for Generating Random Variates
TABLE 15.2.2 K
pg 95%
pg 97.5%
pg 99%
K
pg 95%
pg 97.5%
pg 99%
1 2 3 4 5 6 7 8 9 10 12 14 16 18 20 22 24 26 28 30
3.84146 5.99146 7.81473 9.48773 11.07050 12.59159 14.06714 15.50731 16.91898 18.30704 21.02601 23.68475 26.29620 28.86928 31.41042 33.92443 36.41502 38.88513 41.33713 43.77297
5.02389 7.37776 9.34840 11.14329 12.83250 14.44938 16.01276 17.53455 19.02277 20.48306 23.33660 26.11891 28.84532 31.52636 34.16959 36.78070 39.36407 41.92316 44.46079 46.97924
6.63490 9.21034 11.34487 13.27670 15.08627 16.81189 18.47531 20.09024 21.66599 23.20918 26.21693 29.14122 31.99992 34.80530 37.56623 40.28936 42.97982 45.64168 48.27823 50.89218
35 40 45 50 55 60 65 70 75 80 82 84 86 88 90 92 94 96 98 100
49.80185 55.75848 61.65623 67.50481 73.31149 79.08194 84.82064 90.53122 96.21667 101.87947 104.13874 106.39484 108.64789 110.89800 113.14527 115.38979 117.63165 119.87094 122.10773 124.34211
53.20335 59.34170 65.41016 71.42019 77.38046 83.29767 89.17714 95.02318 100.83934 106.62857 108.93729 111.24226 113.54360 115.84143 118.13589 120.42708 122.71511 125.00007 127.28207 129.56120
57.34207 63.69074 69.95683 76.15389 82.29212 88.37942 94.42208 100.42518 106.39292 112.32879 114.69489 117.05654 119.41390 121.76711 124.11632 126.46165 128.80325 131.14122 133.47567 135.80672
Starting from uniformly distributed random variates, we can generate other random variates given the desired probability distribution. We will discuss three widely used techniques for generating these variates: 1. Inverse transformation 2. Convolution 3. Acceptance –rejection In all the simulations that follow we have used ui U(0,1) distribution shown in Section 15.2 with N ¼ 10,001 points.
FIGURE 15.2.2
15.3
Inverse Transformation Techniques
269
FIGURE 15.2.3
B 15.3
INVERSE TRANSFORMATION TECHNIQUES
One of the more useful ways of generating random variates is through the inverse transformation technique, which is based on the following result. If U is a random variable uniformly distributed in (0,1] with distribution function FU (u) ¼ u,u 1(0,1], and X is a random variable with distribution function FX(x), then the random variates for X can be 21 generated from X ¼ F21 X (U ). We will show that FX (U ) is a random variable with distri21 bution FX(x). Assuming that X ¼ FX (U ), we have P{X x} ¼ P{FX1 (U) x}
(15:3:1)
Since FX (x) is monotonically increasing, we can write Eq. (15.3.1) as follows: P{FX1 (U) x} ¼ P{U FX (x)} ¼ FU {FX (x)}
(15:3:2)
However, U is uniformly distributed in (0,1] and hence FU(u) ¼ u, 0 , u 1. Applying this fact in Eq. (15.3.2), we have FU {FX (x)} ¼ FX (x)
(15:3:3)
This result can be used to obtain the random variates for any distribution function FX(x) only if F21 X can be solved in closed form or FX can be accurately and efficiently inverted numerically. The methodology of finding random variates fXig by the inversion technique can be enumerated as follows: 1. FX(x) is known a priori. 2. Uniform random variates fui, i ¼ 1, . . . , Ng in the interval (0,1] are generated. 3. The inverse F21 X (u) of the cdf FX(x) can be analytically or numerically determined. 4. The variates fxig are generated from xi ¼ F21 X (ui), i ¼ 1, . . . , N. 5. The histogram for fxig is constructed and tested using chi-square goodness of fit. We will use this procedure to find random variates for a number of discrete and continuous distributions starting from 10,001 points of the uniform random variates in (0,1] generated in the previous section.
270
Computer Methods for Generating Random Variates
Discrete Distributions
Since FX(x) is a monotonically increasing function, the inverse for a finitedimensional integer-valued discrete distribution functions can be obtained in a simple manner as shown in the following two examples. Example 15.3.1 given by
The distribution of packets in bytes in a communication system is 8 0, > > > > 0:2, > > < 0:4, FX (x) ¼ 0:6, > > > > 0:8, > > : 1,
0 , x 64 64 , x 128 128 , x 256 256 , x 320 320 , x 384 x . 384
and is shown in Fig. 15.3.1. It is not difficult to find the inverse function F21 X (u). From Fig. 15.3.1, for values of the uniform random variate ui given by 0.4 , ui 0.6, F21 X (u) ¼ 256 bytes. Similarly, we can obtain the complete inverse function as 8 64, 0 , ui 0:2 > > > > < 128, 0:2 , x 0:4 xi ¼ FX1 (ui ) ¼ 256, 0:4 , x 0:6 > > 320, 0:6 , x 0:8 > > : 384, 0:8 , x 1 and the sequence fxig represents the random variates for this discrete distribution. The generated uniform random variates ui are assigned values 64, 128, 256, 320, and 384 depending on the ranges in which they fall along the vertical axis. They are then sorted in ascending order and plotted with x ¼ F21 X (u) along the horizontal axis and i scaled by 1/10,000 along the vertical axis so that the range is from 0 to 1. The plot is shown in Fig. 15.3.2. As an example, the uniform random variates the falling in the shaded region in the figure between 0.4 and 0.6 represent 256 bytes.
FIGURE 15.3.1
15.3
Inverse Transformation Techniques
271
FIGURE 15.3.2
Out of the 10,001 uniform random variates generated, 2017 fall in the range 0– 0.2, 1912 fall in the range 0.2– 0.4, 1976 fall in the range 0.4– 0.6, 2017 fall in the range 0.6 –0.8, and 2079 fall in the range 0.8 –1.0 as shown in Fig. 15.3.2, which is exactly the same as Fig. 15.3.1. Example 15.3.2 In this example, we will simulate a binomial distribution FB(x) with n ¼ 5 and p ¼ 0.4 given by FB (x) ¼
x X
5! (0:4)k (0:6)nk k!(5 k)! k¼0
and shown below and graphed in Fig. 15.3.3:
FIGURE 15.3.3
272
Computer Methods for Generating Random Variates
FIGURE 15.3.4
8 0:07776, > > > > 0:33696, > > < 0:68256, FB (x) ¼ 0:91296, > > > > 0:98976, > > : 1,
0,x1 1,x2 2,x3 3,x4 4,x5 x.5
The inverse function F 21 B (U) can be found in a manner similar to that in the previous example. In Fig. 15.3.3, for values of the uniform random variate ui between 0.33696 and 0.68256, F21 B (u) ¼ 2. Similarly, we can obtain the complete inverse function as 8 0, 0 , ui 0:07776 > > > > 1, 0:07776 , ui 0:33696 > > < 2, 0:33696 , ui 0:68256 FB1 (ui ) ¼ 3, 0:68256 , ui 0:91296 > > > > 4, 0:91296 , ui 0:98976 > > : 1, 0:98976 , ui 1 which is shown in Fig. 15.3.4. 1 The vertical axis has been scaled by 10,000 for the range to correspond to (0,1]. Out of the 10,001 uniform random variates generated, 797 fall in the range 0 – 0.07776, 2555 fall in the range 0.07776 –0.33696, 3404 fall in the range 0.33696 – 0.68256, 2312 fall in the range 0.68256 –0.91296, 801 fall in the range 0.91296 – 0.98976, and 132 fall in the range 0.98976 –1. Figure 15.3.4 is the same as Fig. 15.3.3. Continuous Distributions
If the cumulative distribution function of a random variable can be expressed in a closed form, then it is not difficult to find the inverse in a closed form. We will give several examples to clarify the inverse transformation method. Example 15.3.3 (Exponential Distribution) In this example we will obtain random variates for an exponential distribution given by FX (x) ¼ 1 elx ,
x0
(15:3:4)
15.3
Inverse Transformation Techniques
273
FIGURE 15.3.5
The inverse is given by 1 elx ¼ u
or elx ¼ 1 u
and
1 x ¼ FX1 (u) ¼ ln (1 u), l
x0
The exponential random variates are generated from this equation with l ¼ 13, resulting in the following equation: xi ¼ 3 ln (1 ui ), x 0, i ¼ 0, 1, . . . , 10,000 The histogram subroutine hist(K,Dx,x) is called with K ¼ 100 bins and bin width Dx ¼ 0.1. The resulting histogram is divided by N . Dx ¼ 1000 to yield the normalized histogram fE(xk), k ¼ 1, . . . , 100. This is shown in Fig. 15.3.5. Superimposed on top of the histogram is the true density function, fX (x) ¼ 13 e(x=3) , x . 0. We can see from figure that the fit is very good. We can also apply the chi-square goodness of fit, which it easily passes. Example 15.3.4 (Weibull Distribution)
The cdf of a Weibull distribution is given by a
FX (x) ¼ 1 e½l(xm) ,
x.m
(15:3:5)
This distribution, as mentioned in Chapter 7, is a generalization of the exponential with parameters, scale l, shape a, and location m. Since this is in a closed form, the inverse is given by a a 1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 e½l(xm) ¼ u or e½l(xm) ¼ 1 u and FX1 (u) ¼ m þ a ln(1 u) l with u , 1. The Weibull random variates are generated with l ¼ 13 , a ¼ 3 and m ¼ 2 from the following equation: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi xi ¼ 2 þ 3 3 ln(1 mi ), i ¼ 0, . . . , 10,000 The histogram subroutine hist(K, Dx, x) is called with K ¼ 100 bins and bin width Dx ¼ 0.1. The resulting histogram is divided by N . Dx ¼1000 to yield the normalized histogram fW (xk), k ¼ 1, . . . , 100. This is shown in Fig. 15.3.6. 3Superimposed on 3top of the histogram is the true Weibull density function, fX (x) ¼ 3 13 (x 2)2 e½(x2)=3 , x . 2.
274
Computer Methods for Generating Random Variates
FIGURE 15.3.6
We can see from the figure that the fit is very good. We can also apply the chi-square goodness of fit, which it readily passes. Example 15.3.5 (Rayleigh Distribution) The random variates corresponding to a Rayleigh distribution can be generated by taking (1) the direct inverse of the distribution or (2) the square root of the sum of the squares of two independent Gaussian distributions. The cdf of a Rayleigh distribution is given by FZ (z) ¼ 1 e(z
2
=2s2 )
, z.0
(15:3:6)
Since this is in a closed form, the inverse is found as follows: 2
2
1 e(z =2s ) ¼ u
or e(z
2
=2s2 )
¼ 1 u and
z ¼ FZ1 (u) ¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2s2 ln(1 u)
The Rayleigh random variates are generated from this equation with s2 ¼ 1, resulting in the following equation: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi zi ¼ 2s2 ln(1 ui ), z . 0, i ¼ 0, 1, . . . , 10,000 The histogram subroutine hist(K, Dx, x) is called with K ¼ 200 bins and bin width Dx ¼ 0.05. The resulting histogram is divided by N . Dx ¼ 500 to yield the normalized histogram fR(xk), k ¼ 1, . . . , 200. This is shown in Fig. 15.3.7. Superimposed on top of the 2 histogram is the true density function, fZ (z) ¼ ze(z =2) , z . 0. We can see from figure that the fit is very good. We can also apply the chi-square goodness of fit, which it easily passes. Special Methods
Even though some distributions may not have a closed form, random variates can be obtained by special transformations. The very important Gaussian random variates can be generated by these special transformation techniques. Two independent random variables U and V are both uniformly distributed in (0,1] and whose joint density is given by 1, 0 , u 1, 0 , v 1 fUV (u,v) ¼ (15:3:7) 0, otherwise
15.3
Inverse Transformation Techniques
275
FIGURE 15.3.7
The transformation used to generate standard Gaussian random variables is pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X ¼ 2 ln(U) cos(2pV) pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Y ¼ 2 ln(U) sin(2pV)
(15:3:8)
We can find the joint density fXY (x,y) from the transformation. Solving for u and v in Eq. (15.3.8), we obtain y
1 2 2 tan1 u ¼ e½(x þy )=2 : v ¼ (15:3:9) 2p x The Jacobian matrix of transformation is 2 3 2 cos (2pv) dx dx pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 2 ln(u) 6 du dv 7 6 6 ¼ J¼4 dy dy 5 4 sin (2pv) pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi du dv u 2 ln(u)
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2p sin(2pv) 2 ln(u)
3
7 7 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 5 2p cos(2pv) 2 ln(u)
and the absolute value of the Jacobian determinant is pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cos (2pv) pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2p sin (2pv) 2 ln(u) 2p u 2 ln(u) 2 2 ¼ ¼ 2pe(x þy )=2 kJk ¼ sin (2pv) p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi u pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2p cos (2pv) 2 ln(u) u 2 ln(u) Hence, from Eqs. (15.3.7) and (15.3.11) we have fUV (u,v) 1 (x2 þy2 )=2 1 (x2 =2) 1 (y2 =2) pffiffiffiffiffiffi e ¼ e ¼ pffiffiffiffiffiffi e fXY (x,y) ¼ kJk 2p 2p 2p
(15:3:10)
(15:3:11)
(15:3:12)
which yields two independent standard Gaussian random variables. This transformation is called the Box– Mueller transformation. Example 15.3.6 (Gaussian Distribution) We will now use the Box – Mueller transformation to generate two independent standard Gaussian random variates. In this example, 10,001 points of two independent uniformly distributed random variates fuig and fvig are generated. They are passed through the transformation of Eq. (15.3.8) to form the
276
Computer Methods for Generating Random Variates
Gaussian random variates fzig and fwig. Theoretically, these must be zero mean and unit variance. If they are not, they can be transformed into exactly zero mean and unit variance by another transformation wi w ; i ¼ 0; . . . , 10;000 sw qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi P P where z ¼ (1=N) Ni¼0 zi and sz ¼ (1=N) Ni¼0 (zi z)2 , N ¼ 10,000, with similar definitions for w and sw . The histogram subroutines are called with K ¼ 100 bins and bin width Dx ¼ Dy ¼ 0.08. The resulting histogram is normalized by NDx ¼ NDy ¼ 800 to yield histograms fXk and fYk, k ¼ 1, . . . , 100 for the Gaussian random variates. plots pffiffiffiffiffiffi Both 2 are shown p inffiffiffiffiffiffi Fig. 15.3.8 with the true standard Gaussian fX (x) ¼ (1= 2p)e(x =2) and 2 fY (y) ¼ ð1= 2pÞe(y =2) superimposed on them. From these figures we find that the fit is very good. These random variates are stored for later use in simulating stochastic systems. xi ¼
zi z ; sz
yi ¼
FIGURE 15.3.8
15.3
Inverse Transformation Techniques
277
Example 15.3.7 (Rayleigh Distribution with Phase) We have already found random variates for a Rayleigh distribution using a direct transformation. We will now apply the relationship to two independent standard Gaussian random variables by pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Z ¼ X2 þ Y 2 to generate random variates for the Rayleigh distribution. The Gaussian random variates are generated by the Box – Mueller transformation as explained in the previous section. The Rayleigh random variates and the corresponding phase variates are given by pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 9 zi ¼ x2i þ y2i > = , i ¼ 0, . . . , N y i > wi ¼ tan1 ; xi The sequences fzig and fwig are both passed through histogram subroutines with the number of bins K ¼ 200 and the bin width Dz ¼ Dw ¼ 0.05. Both these histograms are normalized with division by NDz ¼ NDw ¼ 500. The resulting histograms fZk and fWk 2 are shown in Fig. 15.3.9 with the true Rayleigh density fZ (z) ¼ ze(z =2) , z . 0 and uniform density U(– p/2, p/2] superimposed on them. We can see from both plots that the fit is very good. Table 15.3.1 shows the densities with their closed-form distribution functions and their inverses.
FIGURE 15.3.9
278
a b aþ1 , x.0 b xþb
b p½(x a)2 þ b2
1 (x2 þy2 )=2 e 2p
Pareto
Cauchy
Gaussian
z.0
z (z2 =s2 ) e , s2
ala (x m)a1 e½l(xm)
Rayleigh
Weibull
a
x.m
=2s2 )
,
b a , xþb
2
1 2p
1 1
ðx ðy
e(j
2
þh2 )=2
dhdj
x.0
z.0
x.m
1 1 1 x a þ tan 2 p b
1
1 e(z
a
1 e½l(xm) ,
1 lx 1 e , x 0 1 elx , 2 2
l ljxj e , 2
Laplace l0
(1 elx )u(x)
x.0
1 x a 2 a,xb 2 ba 1 x þ a 2b 2 b , x 2b a 1 ba 2
xa ba
cdf F(x)
1 lx e u(x) l
1 ba
pdf f (x)
Exponential
Triangular
Uniform (a,b)
Type
TABLE 15.3.1
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1p a ln (1 u), l
u,1
u,1
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ln (u1 ) cos (2pu2 ) pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi y ¼ 2 ln (u1 ) sin (2pu2 ) x¼
1 a þ b tan p u 2
1 ffiffiffiffiffiffiffiffiffiffiffi b p 1 , a 1u
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2s2 ln (1 u), u , 1
mþ
1 1 1 ln (2u), u ln½2(1 u), l 2 l
1 ln (1 u), u , 1 l u.
pffiffiffiffiffi 1 a þ (b a) 2u, u 2 h pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii 1 b þ (b a) 1 2(1 u) u . 2
a þ u(b a)
Inverse F 21(u)
1 2
15.4
B 15.4
Convolution Techniques
279
CONVOLUTION TECHNIQUES
In cases where it may not be possible to obtain inverses, we can use the convolution technique. From Eq. (13.1.15) we recall that the density of the sum Z of two independent random variables X and Y is the convolution of the densities of X and Y: ð1 fZ (z) ¼
fX (x)fY (z x)dx
(15:4:1)
1
We can generate random variates from independent random variates that have already been generated. We will give two examples. Example 15.4.1 (Triangular Distribution) We want to generate random variates for a triangular distribution. Two independent uniformly distributed random variates, faig and fbig, are generated. We form the sum x i ¼ ai þ bi ,
i ¼ 0, . . . , 10,000
The sequence fxig is passed through the histogram subroutine with the number of bins K ¼ 100 and the bin width Dx ¼ 0.02. The resulting histogram is normalized with NDx ¼ 10,000 0.02 ¼ 200. The histogram fXk is graphed in Fig. 15.4.1. The true triangular density given by fX (x) ¼
x, 2 x,
0,x1 1,x2
is superimposed on the histogram. The fit is very close and passes the chi-square test easily. Example 15.4.2 (Erlang Distribution) The Erlang distribution is obtained by the n-fold convolution of n independent exponential distributions as given in Eq. (7.4.3). Hence we can generate Erlang random variates with n degrees of freedom by summing n independent exponential random variates. We will now generate Erlang random variates with 2 degrees
FIGURE 15.4.1
280
Computer Methods for Generating Random Variates
FIGURE 15.4.2
of freedom. From two sets of uniform random variates faig and fbig, we generate two identically distributed exponential random variates fxig and fyig, with l ¼ 13 given by xi ¼ 3 ln (ai );
yi ¼ 3 ln (bi )
Erlang random variates fzig are formed by summing fxig and fyig. The variates fzig are then passed through the histogram subroutine with number of bins K ¼ 200 and bin width Dz ¼ 0.1. The resulting histogram is normalized by division with NDx ¼ 1000. The histogram fZk is graphed for k ¼ 0, . . . , 200 and shown in Fig. 15.4.2. The true Erlang density for 2 degrees of freedom given by 2 1 fZ (z) ¼ ze(z=3) , 3
z0
is superimposed on the histogram as shown in the figure. The fit is very good as seen in the diagram.
B 15.5
ACCEPTANCE – REJECTION TECHNIQUES
The rejection method does not require that the cumulative distribution function [indefinite integral of p(x)] be readily computable, much less the inverse of that function—which was required for the transformation method in the previous section. Usually the inverse transformation technique uses the cumulative distribution function in closed form, in which case it is the preferred method of generating random variates. However, in many cases the cdf may not be expressible in closed form or it may be very inefficient to calculate it numerically. On the other hand, the density functions may be available in closed form such as the beta or the gamma density. The rejection method is a technique for generating random deviates fxig whose density function fX(x), if known in closed form, is amenable directly to determine these variates. We will now outline the acceptance– rejection method of generating variates. Consider a function g(x) that upper-bounds the density fX(x) or g(x) fX(x) for all x. This is called the bounding or comparison function g(x) which, is not a density function
15.5 Acceptance – Rejection Techniques
281
Ð1 Ð1 since 1 g(x)dx 1 fX (x)dx ¼ 1. We define a normalization constant c ¼ Ð1 1 g(x)dx 1, and another function wX (x) ¼ g(x)=c for all x, thus rendering wX(x) a proper density function. We can now state the acceptance – rejection algorithm: 1. Generate random variates fyi, i ¼ 0, . . . , Ng having the density function wY( y). 2. Generate random variates fui, i ¼ 0, . . . , Ng uniformly distributed in (0,1] and independent of fyig. 3. If ui ½ fX ( yi )=½g( yi ) 1, accept the point yi and set fxi ¼ yig. Otherwise, reject yi. The number of accepted points fxig will be equal to N/c. 4. The sequence fxig are the desired random variates with density function fX(x). We will now show from conditional probability considerations that the generated variates fxig do indeed have the distribution FX(x). From the definition of conditional probability, we obtain P(generated X x) ¼ P(Y x j accept) ¼
P(Y x, acceptÞ P(accept)
(15:5:1)
We need the following result for any y: fX (y) P(accept j Y ¼ y) ¼ P U g(y)
(15:5:2)
Since U is uniformly distributed in (0,1], we have FU(u) ¼ u, and it is independent of Y. Hence Eq. (15.5.2) can be written as follows: fX (y) fX (y) fX (y) P U ¼ FU ¼ (15:5:3) g(y) g(y) g(y) Incorporating Eq. (15.5.3) into Eq. (15.5.2), we obtain P(accept j Y ¼ y) ¼
fX (y) g(y)
(15:5:4)
We can evaluate the denominator in Eq. (15.5.1) from Eq. (15.5.4) with substitution of wY (y) ¼ g(y)=c. ð1 P(accept) ¼ P(accept j Y ¼ y)wY (y)dy 1
ð1 ¼
1
fX (y) g(y) 1 dy ¼ g(y) c c
We will now evaluate the numerator of Eq. (15.5.1): ð1 P(Y x, accept) ¼ P(accept, Y x j Y ¼ y)wY (y)dy
(15:5:5)
(15:5:6)
1
Since Y ¼ y in Eq. (15.5.6) we can rewrite it as ðx P(Y x, accept) ¼ P(accept, y x j Y ¼ y)wY (y)dy 1
(15:5:7)
282
Computer Methods for Generating Random Variates
where the integration is terminated at x because y x. Applying Eq. (15.5.4) and substituting wY (y) ¼ g(y)=c in Eq. (15.5.7), we obtain ðx fX (y) g(y) P(Y x, accept) ¼ dy 1 g(y) c ¼
FX (x) c
(15:5:8)
Combining Eqs. (15.5.5) and (15.5.8), we obtain the result that P(generated X x) ¼
FX (x) c ¼ FX (x) c
(15:5:9)
thus showing that the acceptance –rejection method yields the desired probability distribution. Example 15.5.1 We will give an example of generating beta density variates by the method described above. The general beta density is given by fb (x) ¼
G(a þ b) a1 x (1 x)b1 , G(a)G(b)
0x1
and there is no closed-form solution for the cdf. For a and b integers, we have fb (x) ¼
(a þ b 1)! a1 x (1 x)b1 , 0 x 1 (a 1)!(b 1)!
Random variates will be generated for a beta(3,6) density (Fig. 15.5.1) given by fb (x) ¼ 168x2 (1 x)5 ,
0x1
The maximum of the density function occurs at x ¼ 27 ¼ 0.2857 and fb(27) ¼ 2.55. We can bound this density with a rectangle of width 2.55 so that g(x) ¼ 2.55, 0 ,x 1. Hence the normalizing constant c ¼ 2.55 and wX (x) ¼ g(x)=c is a U(0,1] density function. Algorithm for Acceptance – Rejection 1. Generate a sequence fyi, i ¼ 0, . . . , Ng with uniform density wY (y) ¼ g(y)=c. 2. Generate uniform random variates fui, i ¼ 0, . . . , Ng independent of fyig. 3. If ui ½168y2i (1 yi )5 =2:55; return xi ¼ yi, i ¼ 0, . . . , N. Sequences fyi, i ¼ 0, . . . , 10,000g and fui, i ¼ 0, . . . , 10,000g are generated, and if the condition 3 (above) is satisfied, xi was set equal to yi. The sequence fxig has 3936 points out of the original total of 10,001 points. This corresponds to the expected 10,001/2.55 ¼ 3922 points. The sequence fxig consisting of 3936 points was passed through a histogram subroutine with K ¼ 100 bins and bin width Dx ¼ 0.01. The histogram was normalized by division with 3936 0.01 ¼ 39.36. The normalized histogram is graphed in Fig. 15.5.1 with the true beta(3,6) density superimposed for comparison. The bounding function g(x) is also shown. From the figure it is seen that the fit is good. Discussion
We have discussed three methods for generation of random variates for a given distribution function. The inversion method is used where the cumulative distribution
15.5 Acceptance – Rejection Techniques
283
FIGURE 15.5.1
function can be expressed in a closed form or can be computed efficiently. The convolution method can be used when the density function is expressible as the resultant of a sum of independent random variates. The acceptance –rejection method is used directly on the probability density function. In generating histograms the number of bins K and the bin width Dx have to be manipulated first by “eyeballing” and later confirming that the x value is less than the ta value in the chi-square test at the significance level a. Note also that higher significance levels yield tighter bounds.
CHAPTER
16
Elements of Matrix Algebra
A number of results depend on an understanding of matrices and manipulations of matrices. We will now discuss relevant aspects of matrix algebra. We will assume familiarity with linear vectors spaces [56].
B 16.1
BASIC THEORY OF MATRICES
Definitions
A matrix X is defined as an array of m column vectors {xj , j ¼ 1, . . . , m} with each column vector x.j having n real or complex elements x.j ¼ fx1j, x2j, . . . , xi j, . . . , xn jgT. Thus the matrix X is an n m rectangular array of real or complex numbers and written as 3 2 x1 x2 xj . . . xm 2 36 x11 x12 x1j x1m 7 7 x1 6 7 6 6 x2 76 x21 x22 x2j x2m 7 7 76 (16:1:1) X¼6 7 4 xi 56 6 xi1 xi2 xij xim 7 7 xn 6 5 4 xn1 xn2 xnj xnm Notation: In the double subscript (i j ) the first subscript denotes the row, and the second subscript denotes the column. Thus, an n m array consists of n rows and m columns. Sometimes the matrix X is also written as fxijg. A square matrix is a matrix with equal number of rows and columns. It is denoted as n n square matrix or an n matrix. If the column vectors {xj ¼ ½x1j , x2j , . . . , xnj T , j ¼ 1, 2, . . . , m} are linearly independent, then the matrix X is column-independent. On the other hand, if the row vectors denoted by {xi ¼ ½xi1 , xi2 , . . . , xim , i ¼ 1, 2, . . . , n} are linearly independent, then the matrix X is row-independent. The dots in the column-vector x j and the row-vector x i represent the integers 1, . . . , n. An arbitrary rectangular matrix cannot be both column- and rowindependent unless it is a square matrix. However, not all square matrices need be row-independent and column-independent. Parts of this chapter are based, with permission, on Chapters 30, 31, and 33 from the book Linear Systems Properties—a Quick Reference, by Venkatarama Krishnan, published by CRC Press, 1988.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
284
16.1
Basic Theory of Matrices
285
Matrix Addition
Addition is defined for two matrices X and Y of similar dimensions: X þ Y ¼ {xij } þ {yij } ¼ {xij þ yij }
(16:1:2)
The addition rule is commutative: X þ Y ¼ Y þ X. Example 16.1.1 "
#
"
1
5 2
3
4 7
3
8
6
11
10
16
,
X¼ "
3 4
8
6 9
#
" ,
Y¼
4 6
#
Z¼ 3 2
# ,
XþY¼
2
X þ Z and Y þ Z are not defined
Matrix Multiplication
Matrix multiplication is a little complex. If we denote X Y ¼ Z, then the number of columns of X must be equal to the number of rows of Y. Otherwise, multiplication is not defined. If X is ? n, then Y has to be n ? In this case the dimension of Z is ? ?. The product X Y is defined by Z¼XY¼
n X
xik ykj ¼ {zij}
(16:1:3)
k¼1
Example 16.1.2 "
3
1 2
2
# ,
X¼ 4 "
5 1
4
6 6 Y ¼ 63 4 1
1
3
7 7 27 5 5
34þ13þ21 31þ12þ25
#
XY¼
"
17 15
#
¼ 44þ53þ11 41þ52þ15 2
32 19 3
2
3
43þ14
41þ15
42þ11
16
9
9
6 6 Y X ¼ 63 3 þ 2 4 4 13þ54
31þ25
7 6 7 6 3 2 þ 2 1 7 ¼ 6 17 5 4 12þ51 23
13
7 7 18 7 5 7
11þ55
26
In general, matrix multiplication is not commutative, that is, XY = YX as shown above. As an example, consider the following symmetric matrices: e f a b , Y¼ X¼ f h b d ae þ bf af þ bh ae þ bf be þ df XY ¼ , YX ¼ be þ df bf þ dh af þ bh bf þ dh
286
Elements of Matrix Algebra
The matrices X and Y commute only if af þ bh ¼ be þ df. We usually denote in the product XY that Y is premultiplied by X or X is postmultiplied by Y. Unlike real numbers, division of one matrix by another matrix is not defined.
Transpose of Matrix
The transpose of a matrix X ¼ fxijg is obtained by interchanging the rows and columns and is denoted by XT ¼ {xij }T ¼ {x ji }
(16:1:4)
The transpose of a matrix product (XY) T is given by Y TX T whereas (X þ Y)T ¼ X T þ Y T. If X ¼ X T, then we say that the matrix X is symmetric.
Example 16.1.3
2 X¼ 1
5 3
27 (XY) ¼ 21 T
4 6
2
3 6 Y ¼ 41 4
30 ; 23
3 2 7 1 5;
2 6 XT ¼ 4 5
3
4
27 30 Y X ¼ 21 23 T
T
2
3 1 3 7 3 5 YT ¼ 2 6
1 1
4 3
Determinant of a Matrix
The nonzero determinant of a square matrix is one of the two most important invariants of a matrix. It is usually denoted by det X or jXj. It is computed as det X ¼
n X
xij Dij ¼ x1j D1j þ x2j D2j þ þ xnj Dnj
(16:1:5)
i¼1
with no implied summation over j and Dij is the cofactor of the matrix X defined by Dij ¼ (1)iþj Mij
(16:1:6)
where Mij is the minor of the matrix X obtained by taking the determinant after deleting the ith row, i ¼ 1, . . . , n, and the fixed jth column. Equation (16.1.5) is called a cofactor expansion about the jth column. The difference between the cofactor and the minor is the sign as shown in Eq. (16.1.6).
Example 16.1.4
The determinant of the following matrix is evaluated as follows: 2 3 2 5 3 2 6 1 2 4 57 7 X¼6 4 4 1 3 2 5 3 4 2 1
16.1
Basic Theory of Matrices
287
Expanding in terms of minors about the second column, the expression for the determinant jXj is 2 3 1 4 2 5 jXj ¼ 5 4 3 2 þ 2 4 3 2 3 3 2 1 2 1 2 2 3 2 3 2 5 þ 1 1 4 5 þ 4 1 4 3 4 3 2 2 1 Each determinants in the above equation can again be expanded in terms of minors about the second column as shown by the expansion of the first term: 1 4 5 1 5 4 2 5 4 3 2 ¼ 5 4 5 3 3 3 1 1 3 2 1 1 5 5 (2) ¼ 350 4 2 Evaluating the determinant 42 þ 376 ¼ 632.
by
this
expansion,
we
have
jXj ¼ 350 252 2
If only the determinant of the matrix is zero, we call that it a singular or simply degenerate matrix. In addition to the determinant, if the first- and higher-order minors are also zero, then we call the matrix multiply degenerate. Properties of Determinants (X and Y Nonsingular) 1. det X ¼ det X T: The following properties (2,3,4,5,6) are very useful in the manipulation of matrices. 2. If any row (or column) of X is multiplied by a constant a to yield a new matrix Y, then det X ¼ a . det Y. 3. If any row (or column) of X is proportional to the other one, then the rows (or columns) are linearly dependent and det X ¼ 0. 4. If any two rows (or columns) of X are interchanged an odd number of times to yield Y, then det Y ¼ 2det X. If they are interchanged an even number of times to yield Z, then det Z ¼ det X. 5. If any row (or column) of X is modified by adding to it a times, the corresponding elements of another row (or column) to yield Y, then det Y ¼ det X. (The determinant remains unchanged.) 6. The determinant of a triangular matrix X is the product of its diagonal terms det X ¼ x11.x22 . . . . .xnn. 7. If the matrices X and Y are both n n, then det ( XY) ¼ (det X)(det Y). 8. det (aX) ¼ an det X. 9. Even if X and Y are n n, det (X þ Y) = det X þ det Y in general.
288
Elements of Matrix Algebra
Trace of a Matrix
The trace of a square matrix Tr(X) is the second most important invariant of a matrix. The trace is the sum of the diagonal terms of a matrix. The trace of the matrix given in Example 16.1.4 is 2 þ 2 þ 3 þ 1 ¼ 8.
Rank of a Matrix
The column rank of a rectangular matrix is the number of linearly independent column vectors. Similarly, the row rank of a matrix is the number of linearly independent rows. If the column rank and the row rank are the same, then we call that number the rank of the matrix. Thus in a nonsingular square matrix the column rank equals the row rank and the rank is the dimension of the matrix. In general, rank of any n m matrix can be defined as the number of elements 2, 3, . . . , n or m required to form a nonzero determinant. Forming all possible combinations of determinants from any given matrix is a difficult job. Finding a rank is not always easy, but some methods are easier than others. We show one method for finding the rank and the determinant of any matrix using pivotal 2 2 determinants. Example 16.1.5 2
a11
6 6 a21 6 6 X¼6 6 a31 6 6 a41 4 a51
Find the rank and determinant of the following nonsingular matrix: a12
a13
a14
a22
a23
a24
a32
a33
a34
a42
a43
a44
a52
a53
a54
a15
2
3
1
2
3
2
7 6 6 a25 7 7 6 1 7 6 6 a35 7 7¼6 2 7 6 6 a45 7 5 4 1
1
1
2
1
2
2
2
2
1
2
1
1
2
a55
3
3
7 87 7 7 1 7 7 7 07 5
(16:1:7)
5
Step 1. Remove a11 ¼ 1 outside the matrix in Eq. (16.1.7) using property 2 as shown in the previous section. We form a 4 4 matrix X1 from 2 2 determinants of the form fa11 . ajk 2 aj1 . a1k, j ¼ 2, . . . , 5 : k ¼ 2, . . . , 5) as shown below: 2
3
6 6 3 6 X1 ¼ 1 6 6 0 4 5
4
4
8
6
1
1
5
2
11
3
7 7 7 7 7 3 7 5 1
(16:1:8)
Step 2. Normalize the first element ¼ 3 as in step 1, and the resultant matrix is shown below. 2
1
6 6 3 6 X1 ¼ 1:3 6 6 0 4 5
4 3 8
4 3 6
1
1
5
2
11 3 3 7 7 7 7 7 3 7 5 1
(16:1:9)
16.1
Basic Theory of Matrices
289
Step 3. Form a 3 3 matrix X2 in an exactly analogous fashion by forming 2 2 determinants as in step 1 and normalize the first element in X2 as shown in Eq. (16.1.10) 2 3 2 3 1 4 2 4 1 1 6 7 2 6 7 7 1 1 3 7 ¼ 1:3 4 6 (16:1:10) X2 ¼ 1:3 6 1 1 3 6 7 4 5 14 4 5 14 52 5 52 5 3 3 3 3 3 3 Step 4. Form a 2 2 matrix X3 in an exactly analogous fashion by forming 2 2 determinants as in step 1 and normalize the first element in X3 as shown in Eq. (16.1.11): 2 3 1 " # 4 1 8 1 6 2 7 23 (16:1:11) X3 ¼ 1:3 4 4 5 ¼ 1:3 4 23 19 2 19 6 6 Step 5. Continue this process of reducing the order of the matrix until we end up with either a 1 1 matrix or a matrix whose elements are all zero. Finally we form a 1 1 matrix X4 in an exactly analogous fashion by forming 2 2 determinants as in step 1: X4 ¼ 1:3 4
1 35 ¼ 70 2 3
(16:1:12)
Since we have come to a scalar, the operations stop at this point. The number of cycles is 5 and hence the rank of this matrix is 5. The determinant is given by X4 ¼ 70. Thus, we can obtain both the rank and the determinant by this method. Example 16.1.6 In this example we will determine the rank and the maximum possible determinant of a singular matrix given by 2 3 6 11 5 4 1 6 5 3 2 3 47 6 7 7 X¼6 8 12 4 2 5 6 7 4 6 7 1 0 35 3 0 3 1 2 We form the various matrices as in the previous example, and the results are shown below: 3 2 2 3 11 5 4 1 6 11 5 4 1 1 6 6 6 6 67 6 7 7 6 3 2 3 47 6 5 6 5 3 2 3 47 6 7 7 6 X¼6 2 5 7 6 8 12 4 7¼66 8 12 4 2 5 7 7 6 6 7 7 6 7 1 0 35 4 6 4 6 7 1 0 35 3 0 3 1 2 3 0 3 1 2 2
37 6 6 6 6 8 6 6 X1 ¼ 6 6 3 6 6 4 6 4 11 2
37 6 8 3 4
19 3 22 3 4
11 2
3
3 2 19 1 6 6 7 7 6 6 8 11 7 7 37 6 7 6 6 3 3 7¼6 7 6 6 6 4 2 7 7 6 5 4 11 3 2
2
1 8 3 4 11 2
38 37 22 3 4 3
3 19 37 7 7 11 7 7 7 3 7 7 27 7 35 2
290
Elements of Matrix Algebra
We now form a 3 3 matrix using 2 2 pivotal determinants. This will yield the first column to be all zeros. Interchanging two columns of the resulting matrix is also shown below: 2
3 2 170 85 170 6 7 6 37 37 7 6 6 37 37 37 4 2 7 6 6 4 16 0 16 X2 ¼ 6 7¼6 6 7 6 37 6 6 37 37 4 4 98 98 49 5 0 37 37 37 2 3 1 0 1 6 7 2 7 37 170 6 2 6 4 7 ¼6 07 6 7 6 37 6 37 37 4 98 49 5 0 37 37 0
3 85 0 7 37 7 2 7 07 7 37 5 49 0 37
We now form a 2 2 matrix " 37 170 0 X3 ¼ 6 6 37 0
0
#
"
0
0
0
0
#
¼ 170 0
After three cycles we have a premature termination with a 2 2 zero matrix. Hence 170 the rank of this matrix is 3 and the maximum determinant is 6 37 6 37 ¼ 170:
Inverse of a Matrix
Since division by a matrix is not defined, we define the inverse of a nonsingular matrix as the matrix X 21 such that XX 21 ¼ X 21X ¼ I, an identity matrix. Thus inverse matrices commute with their direct counterparts. The cofactor expansion of the determinant of the matrix X from Eq. (16.1.5) is det X ¼
n X
xij Dij ¼ x1j D1j þ x2j D2j þ þ xnj Dnj
(16:1:5)
i¼1
where the expansion is carried about the n elements in any column j. Equation (16.1.5) can be rewritten as ( n X det X, k ¼ j det X ¼ xij Dik ¼ (16:1:13) 0, k=j i¼1 In Eq. (16.1.13), when k ¼ j, we obtain det X by Eq. (16.1.5), but when k = j, then we have cofactor expansion about the n elements in the jth column but with cofactors obtained from the kth column. This is equivalent to finding the determinant by replacing the kth column with the jth column in the matrix X. Thus X will contain two identical columns and hence from property 3 in the previous section, det X ¼ 0. We divide Eq. (16.1.13) by det X and obtain ( n X 1kn Dik (16:1:14) xij ¼ dkj , det X 1jn i¼1
16.1
Basic Theory of Matrices
291
where dkj is the Kro¨necker delta, which equals 1 if k ¼ j and 0 if k = j. Equation (16.1.14) is a scalar statement of the matrix equation X21X ¼ I, and the inverse is given by 2 3 D11 D21 Dn1 7 Dik 1 6 6 D12 D22 Dn2 7 (16:1:15) X1 ¼ {aki } ¼ xij ¼ 6 .. .. 7 .. det X 4 . det X . 5 . D1n D1n Dnn Note that the cofactors are in reverse order of the sequence of elements of the matrix X. The matrix fDjig of the cofactors is sometimes called the adjoint matrix. Finding the inverse using Eq. (16.1.15) is very inefficient and is of only theoretical interest since it gives an explicit expression for the inverse. Solution of Linear Equations
We can use matrix analysis to solve linear equations of the form Ax ¼ y where x and y are n-vectors: 32 3 2 3 2 y1 a11 a12 a1n x1 7 7 7 6a 6 6 6 21 a22 a2n 7 6 x2 7 6 y2 7 7 7 6 . 6 6 (16:1:16) ¼ .. 7 6 .. 7 6 .. 7 .. 7 6 . . 54 . 5 4 . 5 . 4 . an1
an2
ann A
xn x
yn y
We will assume that A is a nonsingular n n matrix and x and y are n 1 vectors. We want to solve for x in terms of y. Since A is invertible, we can write the solution as x ¼ A1 y This equation can be expanded as 2 3 2 2 32 3 3 D j1 y1 D11 D21 Dn1 x1 n 6 D j2 7 6 x2 7 76 7 1 6 1 X 6 7 6 D12 D22 Dn2 7 6 y2 7 6 7 ¼ 6 .. 7 ¼ 6 .. 6 6 . 7yj 7 7 .. .. .. 5 det A . 4 . 5 det A 4 . 4 4 5 . . . . 5 j¼1 D1n D1n xn Dnn yn D jn x A1 y
(16:1:17)
(16:1:18)
Note again that the order of the cofactors is transposed from that of the original matrix A, that is, if aij is an element in the ith row and jth column, then Dji is in the jth row and ith column. Example 16.1.7 x2, x3, x4:
We shall now solve the following set of linear equations for x1, x1 3x3 2x4 ¼ 4 5 1 15 23 x1 þ x2 þ x3 þ 6x4 ¼ 2 2 2 2 2x1 7x3 6x4 ¼ 11 7 1 25 39 x1 þ x2 þ x3 þ 10x4 ¼ 2 2 2 2
292
Elements of Matrix Algebra
or in matrix form
2
1
0
3
6 5 6 6 2 6 6 6 2 6 4 7 2
1 2 0
15 2 7
1 2
25 2
3 2 2 3 4 72 x 3 6 23 7 7 6 1 6 7 76 7 6 2 7 76 x2 7 6 7 74 5 ¼ 6 7 x3 6 11 7 6 7 7 7 6 5 x4 5 4 39 10 2
The determinant of the matrix A is found as follows: 2 1 0 3 2 3 2 3 2 1 15 5 7 6 5 1 6 7 15 7 6 6 6 2 2 6 7 6 7 6 2 6 2 2 2 7 6 7 6 6 jAj ¼ 6 6 7 3 6 2 7 ¼ 1 6 0 7 7 6 7 6 6 2 0 7 6 7 41 5 4 7 6 25 5 4 10 7 1 25 2 2 2 10 2 2 2 2 3 5 1 15 6 2 2 2 7 6 7 6 7 þ 2 6 2 0 7 7 6 7 4 7 1 25 5 2 2 2
1 2 0 1 2
Or jAj ¼ 1 þ 3 3 ¼ 1. The inverse of matrix A is 2
A1
1
0
3
6 5 6 6 2 6 ¼6 6 2 6 4 7 2
1 2 0
15 2 7
1 2
25 2
2 31 2 1 7 7 6 7 6 16 2 7 7 ¼ 6 1 14 6 7 3 7 5 2 10
2 1 1 1 2
1 2 1 1
The solution to the original set of equations is given by 3 2 2 3 4 2 3 1 2 1 2 6 23 7 2 3 1 x1 7 6 6 2 1 2 17 7 6 7 6 x2 7 6 76 2 17 7 6 6 7 ¼ 6 1 1 1 17 7¼6 4 4 x3 5 6 76 15 7 6 4 3 1 1 56 11 7 1 x4 5 4 1 39 2 2 2 2 Properties of Inverses (X, Y, Z Same Order and Nonsingular) 1. X X21 ¼ X21X matrix commutes with its inverse 2. Xm Xn ¼ X m1n 3. (XY)21 ¼ Y21X21 4. XY ¼ XZ ) Y ¼ Z 5. (X T)21 ¼ (X21)T
3 2 17 7 17 5 1 2
3 6 7 7 7 6 7 7 5 10
16.2 Eigenvalues and Eigenvectors of Matrices
1 det X (1)iþj Mij , where Mij is the minor of X ¼ det X
293
6. det X21 ¼ 7. X1
(16.1.19)
Orthogonal Matrices
The matrix X is orthonormal if X TX ¼ I. Since X21X is also equal to the identity matrix I, we have XT X ¼ I ¼ X1 X
or XT ¼ X1
(16:1:20)
It immediately follows from Eq. (16.1.20) that the determinant of an orthogonal matrix is equal to +1. Note that the converse is not true. In any orthogonal matrix X, real or complex, individual row or column vectors x are orthonormal: 1, j ¼ i H xi x j ¼ (16:1:21) 0, j = i where H stands for conjugate transpose. An example of a 4 4 orthogonal matrix used in discrete Fourier transform analysis is shown below: 2 3 1 1 1 1 6 1 ej(2p=4) ej(4p=4) ej(6p=4) 7 7 W¼6 (16:1:22) 4 1 ej(4p=4) ej(8p=4) ej(12p=4) 5 1 ej(6p=4) ej(12p=4) ej(18p=4) It can be verified that the rows and columns of W are orthonormal and det W ¼ 1.
B 16.2 EIGENVALUES AND EIGENVECTORS OF MATRICES Eigenvalues
The concept of eigenvalues and eigenvectors plays an important role. We motivate this concept by investigating solutions to a linear system of equations given by Ax ¼ lIx ¼ lx where A is an n n matrix and x is an n 1 vector, l is a scalar constant, and I is an n n identity matrix. Simplification of the equation above yields (lI A)x ¼ 0
(16:2:1)
A trivial solution to Eq. (16.2.1) is x ¼ 0. Nontrivial solutions can exist only if det(lI A) ¼ jlI Aj ¼ 0
(16:2:2)
Equation (16.2.2) is called the characteristic equation of the matrix A. Expansion of the determinant in Eq. (16.2.2) yields a polynomial in l of degree n called the characteristic polynomial of the matrix A, given by ln þ an1 ln1 þ an2 ln2 þ þ a0 ¼ 0
(16:2:3)
294
Elements of Matrix Algebra
The characteristic equation has a fundamental significance in linear systems. The n roots of the characteristic equation are called the eigenvalues of the matrix A, and in linear systems they are also known as the “poles” of the transfer function. The eigenvalues can be real, repeated, or complex. From the well-known result for the roots of polynomials, we also have the sum of the eigenvalues as 2an21, and the product of the eigenvalues, which is also the determinant of the matrix A, is given by (21)n a0. Example 16.2.1 determined:
The eigenvalues of the following nonsingular matrix have to be 2
3
1:5625
4:5
2:0625
5:25
6 0:4375 6 6 A¼6 6 0:4375 6 4 0:0625
2:5
1:9375
6:25
2:5
1:9375
7:25
3:5
0:4375
6:25
1:25 7 7 7 1:25 7 7 7 0:25 5
1:5
3
1
0:5
0
0:75
The characteristic equation of A is det (lI 2 A)¼0 is given by 2 l þ 1:5625 4:5 6 6 0:4375 l þ 2:5 6 6 jlI Aj ¼ 6 0:4375 2:5 6 6 6 3:5 4 0:0625 0:5 0 5
4
3
2:0625
5:25
1:9375
6:25
l þ 1:9375
7:25
0:4375 1:5
l 6:25 3
3 0:75 7 1:25 7 7 7 1:25 7 7 7 0:25 7 5 l1
2
¼ l 1:25l 2:375l þ l þ 1:375l þ 0:25 ¼ 0 The solutions of the fifth-order polynomial of this equation are the eigenvalues of A, given by l1 ¼ 2, l2 ¼ 1, l3 ¼ 1, l4 ¼ 0:5, l5 ¼ 0:25 The product of the eigenvalues is the determinant given by (21)5 . (a0) ¼ 20.25 and the sum of the eigenvalues is a4 ¼ 21.25. We can also find the eigenvalues of a square matrix that is singular. In this case the number of nonzero eigenvalues represents the rank of the matrix and the number of zero eigenvalues, the degree of singularity.
Cayley– Hamilton Theorem
The Cayley– Hamilton theorem states that every square matrix satisfies its own characteristic equation. In Eq. (16.2.3), if we substitute the matrix A for l, we obtain the Cayley –Hamilton equation An þ an1 An1 þ an2 An2 þ þ a0 A0 ¼ 0 where 0 represents a zero matrix.
(16:2:4)
16.2 Eigenvalues and Eigenvectors of Matrices
Example 16.2.2
295
Given the matrix 2
0 A¼4 6 6
1 0 11
3 0 15 6
equation is l3 þ 6l2 þ 11l þ 6 ¼ 0. According to the Cayley– we should have 33 2 2 32 31 0 0 1 0 0 1 0 7 6 6 7 7 1 5 þ 64 0 0 1 5 þ 114 0 0 15 6 6 11 6 6 11 6 30 2 3 2 3 1 0 6 11 6 0 0 6 7 6 7 6 7 0 1 5 ¼ 4 36 60 25 5 þ 4 36 66 36 5 11 6 150 239 90 216 360 150 3 2 3 11 0 6 0 0 7 6 7 0 11 5 þ 4 0 6 0 5 121 66 0 0 6
the characteristic Hamilton theorem, 2 0 1 6 0 4 0 6
11 2 0 6 þ 64 0 6 2 0 6 þ4 0 66
or
2
0 ¼ 40 0
0 0 0
3 0 05 0
showing the validity of the theorem. Definiteness of Matrices
A matrix A is positive definite if all its eigenvalues are either positive or have positive real parts. For example, the matrix A in Example 16.2.2 is positive definite, whereas the matrix A in Example 16.2.1 is nondefinite. Similarly, matrices having eigenvalues that are negative or have negative real parts are known as negative definite. If some of the eigenvalues are zero, then the matrix is called positive semidefinite or negative semidefinite accordingly. Since the determinants are (21)n times the product of eigenvalues, definiteness can also be defined by the sign of the determinants of all principal minors of a matrix. Principal minors are those minors obtained by successively deleting the rows and columns of a matrix starting from the first row and first column. If all the determinants of principal minors are positive, then the matrix is positive-definite: if they are negative then they are negative definite. Example 16.2.3 eigenvalues:
We will check the definiteness of the following matrix by finding the 2
1
3
7
7
5
6
9
11
4
87 7 7 87 7 7 75
63 6 6 A¼6 63 6 4 2:5
9
11
3
8
9:5
4
0:5
0
1:5
3
1
296
Elements of Matrix Algebra
As in the previous example, setting det (lI 2 A) ¼ 0 gives 3 2 7 5 6 l 1 7 6 3 l9 11 4 8 7 6 7 6 7 2 3 2 9 l þ 11 3 8 7 jlI Aj ¼ 6 6 3 7 ¼ l (l þ 4l þ l 6) ¼ 0 6 7 4 2:5 8 9:5 l þ 4 7 5 0:5 0 1:5 3 l1 Thus the rank of the matrix A is 3 since there are two zero eigenvalues. The eigenvalues are given by l1 ¼ 23, l2 ¼ 22, l3 ¼ 1, l4 ¼ 0, l5 ¼ 0. Here the matrix A is nondefinite since it has positive, negative, and zero eigenvalues.
Eigenvectors
Eigenvectors are solutions to Eq. (16.2.1) for any given eigenvalue. We shall assume that all eigenvalues are distinct and there are no repeated eigenvalues. If we substitute any eigenvalue li in det(lI 2 A) ¼ 0, then, by the very definition, we have det(liI– A) ¼ 0 and hence solutions to Eq. (16.2.1) are possible, and these solutions are the unnormalized eigenvectors ci. However, these solutions are unique only upto a multiplicative constant. To make the solutions unique we can normalize these eigenvectors and the normalized eigenvectors are denoted by fi ¼ ci/jcij. Example 16.2.4 repeated below:
Find the eigenvectors of the nonsingular matrix of Example 16.2.1, 2
2:0625
6 0:4375 2:5 6 6 A¼6 6 0:4375 2:5 6 4 0:0625 3:5
1:9375
6:25
1:9375
7:25
0:4375
6:25
1:25 7 7 7 1:25 7 7 7 0:25 5
1:5
3
1
0:5
0
5:25 0:75
3
1:5625 4:5
Substituting l1 ¼ 2 in this equation, we can solve for the unnormalized eigenvectors: 2 32 3 c11 3:5625 4:5 2:0625 5:25 0:75 6 0:4375 4:5 6 7 1:9375 6:25 1:25 7 6 76 c12 7 6 76 7 6 7 3:9375 7:25 1:25 7 ðl1 I AÞC1 ¼ 6 6 0:4375 2:5 76 c13 7 ¼ 0 6 76 7 4 0:0625 3:5 0:4375 4:25 0:25 54 c14 5 0:5
0
1:5
3
1
c15
As mentioned earlier, the equation above can be solved for only four of the unknowns in terms of the fifth. Let us assume that c11 ¼ 1 and solve for c12,c13,c14,c15 in terms of c11. The preceding equation can be rewritten by substituting c11 ¼ 1: 3 32 2 3 2 c12 0:4375 4:5 1:9375 6:25 1:25 7 76 6 7 6 6 2:5 3:9375 7:25 1:25 76 c13 7 6 0:4375 7 7 76 6 7¼6 6 3:5 0:4375 4:25 0:25 76 c 7 6 0:0625 7 5 54 14 5 4 4 0:5 0 1:5 3 1 c15
16.2 Eigenvalues and Eigenvectors of Matrices
297
Solving for c12 ,c13 ,c14 ,c15 and adjoining c11 ¼ 1, we obtain the unnormalized eigenvector corresponding to the eigenvalue l1 ¼ 2: 3 2 3 2 c11 1 3 2 3 2 1:333333 c12 7 6 7 6 6 c12 7 6 1:333333 7 6 c 7 6 1:952381 7 6 7 6 7 7 6 13 7 6 ¼6 1:952381 7 c13 7 7 and 6 6 7¼6 6 7 6 7 4 c14 5 4 1:238095 5 7 6 7 6 4 c14 5 4 1:238095 5 1:285714 c15 1:285714 c15 Normalization of this equation yields the eigenvector f1: 3 2 3 2 2 3 0:319838 1 c11 7 6 7 6 6 7 6 1:333333 7 6 0:426451 7 6 c12 7 7 6 7 6 7 c1 6 1 6 1:952381 7 ¼ 6 0:624446 7 6 c13 7 ¼ f1 ¼ 6 7 6 7 6 7 jc1 j 6 7 7 6 7 3:126581 6 4 1:238095 5 4 0:395990 5 4 c14 5 0:411220 1:285714 c15 l1 ¼2
In a similar manner, the other eigenvectors corresponding to l2 ¼ 1, l3 ¼ 21, l4 ¼ 20.5, and l5 ¼ 20.25 are shown below: 2 2 2 3 3 3 0:275010 0:774597 0:679189 6 6 6 7 7 7 6 0:366679 7 6 0:516398 7 6 0:549819 7 6 6 6 7 7 7 6 6 7 7 7 f2 ¼ 6 6 0:641681 7 , f3 ¼ 6 0:258199 7 , f4 ¼ 6 0:032342 7 , 6 6 6 7 7 7 4 0:275010 5 4 0:258199 5 4 0:291081 5 0:550019 0 0:388108 2
l2 ¼1
3
l3 ¼1
l4 ¼0:5
0:679104 6 7 6 0:325871 7 6 7 7 f5 ¼ 6 6 0:340796 7 6 7 4 0:166667 5 0:531373 l5 ¼0:25
Modal Matrix
A matrix F consisting of the linearly independent eigenvectors of a matrix A is called the modal matrix of A. In the previous example the modal matrix of A was 2 3 0:319838 0:319838 0:774597 0:679189 0:679104 6 7 0:516398 0:549819 0:325871 7 6 0:426451 0:426451 6 7 F¼6 0:258199 0:032342 0:340796 7 6 0:624446 0:624446 7 6 7 0:258199 0:291081 0:166667 5 4 0:395990 0:395990 0:411220 0:411220 0 0:388108 0:531373 The determinant of F ¼ 20.00141.
298
Elements of Matrix Algebra
Diagonalization of Matrices
An n n square matrix A is diagonalizable if there exists another nonsingular n n matrix T such that T21AT ¼ D, where D is a diagonal matrix. The conditions of diagonalizability reduce to the existence of n linearly independent eigenvectors of A or the modal matrix F of A is invertible, in which case the jth diagonal element of D is the jth eigenvalue of A. In this case the modal matrix F is the desired matrix T. The modal matrix F is not the only matrix that will diagonalize A. There are other matrices that may also diagonalize A, but all the diagonal elements obtained from the modal matrix only are the eigenvalues of A. As a consequence of the preceding discussion, we can write the matrix equivalent of the eigenequation (16.2.1) as follows: or F1 AF ¼ L
AF ¼ FL Example 16.2.5
Diagonalize the following matrix: 2 0 1 0 6 4 0 0 A¼6 4 0 2 0 5 1 1
(16:2:5)
3 0 07 7 15 0
The eigenvalues of A are obtained by solving the determinant equation jlI 2 Aj ¼ 0, or 2 3 l 1 0 0 6 4 l 0 07 7 ¼ l4 5l2 þ 4 ¼ 0 jlI Aj ¼ 6 4 0 2 5 l 1 5 1 1 l The characteristic equation can be factored as (l 2 2)(l þ 2)(l 2 1)(l þ 1) ¼ 0, yielding the eigenvalues as l1 ¼ 2, l2 ¼ 22, l3 ¼ 1, l4 ¼ 21. We find the eigenvectors corresponding to these eigenvalues. The eigenvectors following the Example 16.2.4 are 3 3 2 2 3 3 3 3 2 2 p ffiffiffiffiffi p ffiffiffiffiffiffiffi ffi 6 74 7 6 146 7 0 0 7 7 6 6 6 6 7 6 6 7 7 7 6 6 6 pffiffiffiffiffi 7 6 pffiffiffiffiffiffiffiffi 7 6 0 7 6 0 7 7 7 6 6 7 7 6 6 6 74 7 6 146 7 6 1 7 6 1 7 f1 ¼ 6 7, f2 ¼ 6 1 7, f3 ¼ 6 pffiffiffi 7, f4 ¼ 6 pffiffiffi 7 7 6 5 7 6 6 27 6 27 6 pffiffiffiffiffi 7 6 pffiffiffiffiffiffiffiffi 7 7 7 6 6 6 74 7 6 146 7 4 1 5 4 1 5 7 7 6 6 pffiffiffi pffiffiffi 4 2 5 4 10 5 2 2 pffiffiffiffiffiffiffiffi pffiffiffiffiffi 146 74 or 2
0:348743
3
2
0:248282
3
6 0:697486 7 6 0:496564 7 6 7 6 7 f1 ¼ 6 7 , f2 ¼ 6 7, 4 0:581238 5 4 0:082761 5 0:827606
0:232495 l1 ¼2
2
l2 ¼2
3
0 6 7 0 6 7 f4 ¼ 6 7 4 0:707107 5 0:707107 l4 ¼1
2
0
3
6 7 0 6 7 f3 ¼ 6 7, 4 0:707107 5 0:707107 l3 ¼1
16.2 Eigenvalues and Eigenvectors of Matrices
299
The modal matrix F is given by 2
F ¼ ½f1
f2
f3
3 6 pffiffiffiffiffi 6 74 6 6 6 pffiffiffiffiffi 6 6 74 f4 ¼ 6 6 5 6 pffiffiffiffiffi 6 74 6 4 2 pffiffiffiffiffi 74
3 pffiffiffiffiffiffiffiffi 0 146 6 pffiffiffiffiffiffiffiffi 0 146 1 1 pffiffiffiffiffiffiffiffi pffiffiffi 146 2 10 1 pffiffiffiffiffiffiffiffi pffiffiffi 146 2
3 0 7 7 7 0 7 7 7 7 1 7 pffiffiffi 7 27 7 1 5 pffiffiffi 2
and 2
2 6 0 F1 AF ¼ 6 40 0
0 2 0 0
0 0 1 0
3 0 07 7 05 1
Symmetric Matrices
Real symmetric matrices are those matrices that satisfy the condition AT ¼ A
(16:2:6)
Complex symmetric matrices satisfy the Hermitian symmetry condition AH ¼ (A )T ¼ A
(16:2:7)
where the symbol H defined earlier stands for the conjugate transpose. Symmetric matrices play a very important role in probability; for example, the covariance matrices are all real symmetric matrices. They have some important properties that come in handy in simplifying algebraic manipulations. Some of the more important properties of symmetric matrices are 1. All eigenvalues are real. 2. The eigenvectors corresponding to distinct eigenvalues are orthogonal. Hence the eigenvectors can form the basis vectors of an n-dimensional coordinate system. 3. The modal matrix F is an orthogonal matrix, and hence F21 ¼ F T. The diagonalization of a symmetric matrix can be accomplished by the transformation F TAF ¼ L. Example 16.2.6 The eigenvectors and eigenvalues of the symmetric matrix given below are to be determined and their properties studied: 2 3 1 2 1 1 6 2 1 1 1 7 7 A¼6 4 1 1 2 2 5 1 1 2 0 The characteristic equation is det [lI 2 A] ¼ 0: l 1 2 1 1 2 l 1 1 1 jlI Aj ¼ ¼ l4 4l3 7l2 þ 22l þ 24 ¼ 0 1 1 l 2 2 1 1 2 l
300
Elements of Matrix Algebra
The eigenvalues are l1 ¼ 3, l2 ¼ 22, l3 ¼ 21, l4 ¼ 4, obtained by factoring the characteristic equation. The four normalized eigenvectors are obtained as outlined in the previous examples and they are given by 2 2 3 2 2 3 3 3 1 1 1 1 7 7 7 7 1 6 1 6 1 6 1 6 6 17 617 6 1 7 6 17 f1 ¼ pffiffiffi 6 f2 ¼ pffiffiffi 6 7 , f3 ¼ pffiffiffiffiffi 6 7, 7 , f4 ¼ pffiffiffiffiffi 6 7 3405 10 4 2 5 24 05 15 4 3 5 0
1
l1 ¼3
2
l2 ¼2
2
l3 ¼1
l4 ¼4
The modal matrix F is 2
1 1 0 1
1 1 1 1 1 6 1 6 F ¼ pffiffiffi pffiffiffi pffiffiffiffiffi pffiffiffiffiffi 4 0 2 3 10 15 0 It can be verified that
fi fj ¼
1, 0,
1 1 2 2
3 1 17 7, 35 2
jFj ¼
30 ¼1 30
j¼i j=i
showing that the modal matrix is orthogonal and jFj ¼ 1. The original matrix A can be diagonalized by the modal matrix as shown below: 2
1 6 1 6 1 FT AF ¼ 6 30 4 1
1 1
0 0
1
2
1
1
3
2
1 6 1 6 6 4 0 0
1 1 1 1 0 1
2 2
32 1 0 6 7 1 76 2 76 2 54 1
2 1
1 1
1
2
3 1 1 7 7 7 2 5
1 1 2 0 2 3 2 3 1 3 0 0 0 6 0 2 17 0 07 7 1 6 7 ¼6 7 7 3 5 30 4 0 0 1 0 5 2 0 0 0 4
Similarity Transformations
In the previous section we obtained the diagonal matrix L by transforming the matrix A by the modal matrix F. The resulting diagonal matrix L has all the fundamental properties of the original matrix A. For example these two matrices have the same determinants and the same traces. Such matrices are called similar matrices, and the transformation is known as a similarity transformation. Any nonsingular matrix A can be transformed into another similar matrix B by the following similarity transformation T1 AT ¼ B where T is another nonsingular transforming matrix. Example 16.2.7 With 2 0 1 6 1 0 T¼6 4 1 1 1 1
1 1 1 1
3 0 17 7 05 1
2
and
1 2 6 2 1 A¼6 4 1 1 2 1
3 1 2 2 17 7 1 05 1 2
16.3
Vector and Matrix Differentiation
301
we have 2
7 6 2 1 T AT ¼ B ¼ 6 4 7 2
0 7 2 2 4 6 3 1
3 5 37 7 75 2
In this example the determinants of A and B are 1 and the traces of A and B are 21.
B 16.3
VECTOR AND MATRIX DIFFERENTIATION
The definitions for differentiation with respect to a real vector and a complex vector are slightly different. In the case of differentiation of a complex vector, the question of whether the derivative is a total or partial also makes a difference. We shall first discuss differentiation with respect to real vectors.
Differentiation with Respect to Real Vectors
Let x be an n-vector variable defined by x ¼ fx1, x2, . . . , xngT. The total and partial derivative operators with respect to x are defined by 2 2 3 3 d @ 6 dx1 7 6 @x1 7 6 6 7 7 6 d 7 6 @ 7 d @ 6 7, 7 ¼6 ¼ (16:3:1) 6 7 7 dx 6 6 dx2 7 @x 6 @x2 7 4 d 5 4 @ 5 @xn dxn Just as the (d=dx)x ¼ 1, we want the derivative with respect to the vector x to be equal to the identity matrix I, taking care that the dimensional compatibility conditions are satisfied. Thus, (d=dx)x cannot be defined since the compatibility conditions are not met. We can, however, define (d=dx)xT ¼ I as shown below: 2 3 d 6 dx1 7 2 3 6 7 1 0 0 7
d T 6 d 7 4 5 x ¼6 (16:3:2) 6 dx2 7 x1 x2 xn ¼ 0 1 0 ¼ I dx 6 7 0 0 0 1 4 d 5 dxn In an analogous fashion we can define the following operation: 2 3 d 6 dx1 7 2 3 6 7 2x1 6 7
d T d 7 2 2 2 4 5 x x¼6 6 dx2 7 x1 þ x2 þ þ xn ¼ 2x2 ¼ 2x dx 6 7 2x n 4 d 5 dxn
(16:3:3)
302
Elements of Matrix Algebra
We can also write the following differentiation formulas as follows d T x Ax ¼ (A þ AT )x dx d T d y Ax ¼ xT AT y ¼ AT y dx dx
(16:3:4)
Note that in Eq. (16.3.4) x TAx is a scalar, known as the quadratic form, and y TAx is also a scalar, known as the bilinear form. A scalar is its own transpose, that is, x TAx ¼ x TA Tx and y TAx ¼ x TA Ty. Corresponding to Eq. (16.3.2), we can also define the following operations: 2 3 x1 6 x 7 T 27 d d d d 6 6 . 7¼n x¼ 7 . dx dx1 dx2 dxn 6 4 . 5 xn
d dx
T
xxT ¼
d dx1 2
d dx2 x21
x1 x2
6x x 2 6 2 1 x2 6 6 . .. 4 .. . xn x1 xn x2 T d d d xyT ¼ dx dx1 dx2 2 x1 y1 x1 y2 6x y x y 6 2 1 2 2 6 .. 6 .. 4 . . xn y1 xn y2
d dxn
3
x1 xn
x2 xn 7 7 T .. 7 7 ¼ (n þ 1)x . 5
d dxn
(16:3:5)
x2n
x1 yn
3
x2 yn 7 7 T .. 7 7 ¼ ny . 5 xn yn
Differentiate f(x) with respect to the vector x: " #" # x1
3 2 T f (x) ¼ x Ax ¼ x1 x2 ¼ 3x21 þ 6x1 x2 þ 5x22 x2 4 5
Example 16.3.1
Differentiating f(x) with respect to x1 and x2, we obtain 9 d " > (3x21 þ 6x1 x2 þ 5x22 ) ¼ 6x1 þ 6x2 > = 6 dx1 ¼ d > 6 > (3x21 þ 6x1 x2 þ 5x22 ) ¼ 6x1 þ 10x2 ; dx2 Using the formula ½d(xT Ax)=dx ¼ ½A þ AT x, we (" # " 3 2 3 d T f (x) ¼ ½A þ A x ¼ þ dx 4 5 2
obtain # )" # 4 x1 5
x2
6
#"
10
"
x1
#
x2
6
6
6
10
¼
#"
x1 x2
#
16.3
Vector and Matrix Differentiation
303
We differentiate f (x, y) with respect to the vector x in the following
Example 16.3.2 example: T
f (x, y) ¼ y Ax ¼ y1
3 y2 4
2 5
x1 x2
¼ 3x1 y1 þ 4x1 y2 þ 2x2 y1 þ 5x2 y2
Using the formula ½d(yT Ax)=dx ¼ AT y, we can write 3 4 y1 3y1 þ 4y2 ¼ AT y ¼ 2 5 y2 2y1 þ 5y2 Complex Differentiation Total Derivatives Differentiation with respect to a complex variable z ¼ x þ jy is a bit more involved. If we have a function of a complex variable
w ¼ f (z) ¼ u(x, y) þ jv(x, y)
(16:3:6)
then the total derivative of f(z) with respect to z is defined by d Dw f (z þ Dz) f (z) w ¼ lim ¼ lim Dz!0 Dz Dz!0 dz Dz
(16:3:7)
If the derivative df (z)=dz is to exist, then the limit must exist independent of the way in which Dz approaches zero. This condition imposes restrictions on the existence of the total derivative. With Dw ¼ Du þ j Dv and Dz ¼ Dx þ j Dy, we have the following from Eq. (16.3.7): lim Dz ¼ lim Dz!0
Dx!0 Dy!0
Du þ jDv Dx þ jDy
(16:3:8)
If we first let Dy ! 0 and then let Dx ! 0 in Eq. (16.3.8), we have df (z) @u @v ¼ þj dz @x @x
(16:3:9)
Similarly, if we first let Dx ! 0 and then let Dy ! 0, we obtain df (z) @v @u ¼ j dz @y @y
(16:3:10)
If the derivative df (z)=dz is to be unique then Eqs. (16.3.9) and (16.3.10) must match. Hence @u @v ¼ @x @y @v @u ¼ @x @y
(16:3:11)
These conditions are known as the Cauchy –Riemann (C– R) conditions. If C – R conditions are satisfied, then the derivative of f (z) is given by either Eq. (16.3.9) or Eq. (16.3.10).
304
Elements of Matrix Algebra
A complex function f (z) is said to be analytic in the neighborhood of z ¼ z0, if C –R conditions are satisfied in that neighborhood. In addition, if it is also analytic at z ¼ z0, then the point z0 is a regular point. However, if it is not analytic at z ¼ z0, then z0 is called a singular point. Example 16.3.3 We will find the derivative of f (z) ¼ z 2 ¼ (x þ jy)2 ¼ x 2 2 y 2 þ 2jxy. We will determine whether f (z) satisfies the Cauchy – Riemann conditions of Eq. (16.3.9). z2 ¼ (x þ jy)2 ¼ x2 y2 þ 2jxy u(x, y) ¼ x2 y2
:
v(x, y) ¼ 2xy
@u @v ¼ 2x ¼ ¼ 2x @x @y and hence
@v @u ¼ 2y ¼ ¼ 2y @x @y
The C – R conditions are satisfied and df (z)=dz is given by d 2 @u @v z ¼ þ j ¼ 2(x þ jy) ¼ 2z dz @x @x
Example 16.3.4 On the other hand, we will find the derivative of f (z) ¼ z 2 ¼ (x 2 jy)2 ¼ x 2 2 y 2 2 2jxy. We will determine whether f (z) satisfies the Cauchy – Riemann conditions of Eq. (16.3.9): z2 ¼ (x jy)2 ¼ x2 y2 2jxy u(x, y) ¼ x2 y2
: v(x, y) ¼ 2xy
@u @v ¼ 2x = ¼ 2x @x @y @v @u ¼ 2y = ¼ 2y @x @y Since the C –R conditions are not satisfied at points other than at z ¼ 0, z 2 does not have a total derivative dz2 =dz at points other than at z ¼ 0. Partial Derivatives Many functions occurring in engineering practice are not analytic, and we are interested in optimization problems involving partial derivatives with respect to a complex variable z. With z ¼ x þ jy, we shall define the partial derivative operator @=@z as
@ @ @ ¼ þj @z @x @y
(16:3:12)
16.3
Vector and Matrix Differentiation
305
From the definition given in Eq. (16.3.12), we have the following: @ @ @ z¼ þj (x þ jy) ¼ 1 1 ¼ 0 @z @x @y @ @ @ z ¼ þj (x jy) ¼ 1 þ 1 ¼ 2 @z @x @y
(16:3:13)
Note that from Example 16.3.3 the total derivative (d/dz)z does not exist since z is not analytic except at z ¼ 0. From Eq. (16.3.12) we can also write @ @ @ (z z) ¼ þj (x2 þ y2 ) @z @x @y ¼ 2(x þ jy) ¼ 2z @ 2 @ @ z ¼ þj (x2 þ 2jxy y2 ) @z @x @y ¼ 2(x þ jy) 2(x þ jy) ¼ 0 @ @ @ (z w) ¼ þj (xu þ yv þ j(xv yu)Þ @z @x @y
(16:3:14)
¼ 2(u þ jv) ¼ 2w @ @ @ (zw) ¼ þj (xu yv þ j(xv þ yu)Þ @z @x @y ¼ (u þ jv) (u þ jv) ¼ 0 Note that if we used the chain rule of differentiation in Eq. (16.3.14) and apply Eq. (16.3.13), we obtain the same results. This fact gives credibility to the definition of the partial derivative @=@z. Note also that if z and w were real, then we would get (@=@z)z2 ¼ 2z and (@=@z)zw ¼ w. These differences also manifest themselves in defining vector differentiation with respect to a complex variable.
Vector Partial Differentiation Analogous to Eq. (16.3.12), partial derivatives with respect to a vector complex variable can be defined. With z ¼ [x1 þ jy1, x2 þ jy2, . . . , xn þ jyn]T, partial derivatives @=@z and @=@z are defined as follows:
3 @ @ þj 6 @x1 @y1 7 7 6 6 @ @ 7 7 6 þj @ 6 @y2 7 ¼ 6 @x2 7; 7 @z 6 .. 7 6 . 7 6 4 @ @ 5 þj @xn @yn 2
2
x1 þ jy1
3
6 x þ jy 7 27 6 2 7; z¼6 .. 6 7 4 5 . xn þ jyn
3 @ @ j 6 @x1 @y1 7 7 6 6 @ @ 7 7 6 @ 6 @x2 j @y2 7 ¼ 7 6 7 @z 6 .. 7 6 . 7 6 4 @ @ 5 j @xn @yn 2
(16:3:15)
306
Elements of Matrix Algebra
With this definition of @=@z and @=@z , we can define the following operations: 3 @ @ þj 6 @x1 @y1 7 7 6 6 @ @ 7 7 6 þ j @ H 6 @x @y2 7 z ¼6 2 7 x1 jy1 7 6 @z .. 7 6 . 7 6 4 @ @ 5 þj @xn @yn 2 3 2 0 0 0 60 2 0 07 6 7 ¼6 7 ¼ 2In 40 0 2 05 2
0
0 0
x2 jy2
xn jyn
(16:3:16)
2
where z H is the conjugate transpose of z and In is an nth-order identity matrix. In a similar manner, we obtain 3 @ @ j 6 @x1 @y1 7 7 6 6 @ @ 7 7 6 j @ T 6 @x @y2 7 2 z ¼ 7 x1 þ jy1 6 7 6 @z .. 7 6 . 7 6 4 @ @ 5 j @xn @yn 2 3 2 0 0 0 60 2 0 07 6 7 ¼6 7 ¼ 2In 40 0 2 05 2
0
0
0
x2 þ jy2
xn þ jyn
(16:3:17)
2
In addition, we also have 3 @ @ þj 6 @x1 @y1 7 7 6 6 @ @ 7 7 6 þ j @ T 6 @x @y2 7 z ¼6 2 7 x1 þ jy1 7 6 @z .. 7 6 . 7 6 4 @ @ 5 þj @xn @yn 2
¼ 0n ¼
x2 þ jy2
xn þ jyn
(16:3:18)
@ H z @z
where 0n is an nth-order zero matrix. Equations (16.3.16) – (16.3.18) are very useful in complex vector minimization of scalar quadratic error performance criteria, as we will show in the following examples. Example 16.3.5 The terms z and w are two complex 2-vectors as shown below. It is desired to minimize the complex matrix scalar criteria 11 ¼ zH w and 12 ¼ wH z with
16.3
respect to the complex vector z. With a þ jb ; z¼ c þ jd
Vector and Matrix Differentiation
w¼
e þ jf g þ jh
307
and the corresponding error criteria
11 ¼ zH w ¼ ae þ bf þ cg þ dh þ j(af be þ ch dg) 12 ¼ wH z ¼ ae þ bf þ cg þ dh j(af be þ ch dg) we can differentiate 11 and 12 partially with respect to z, and using Eqs. (16.3.16) and (16.3.18), we obtain e þ e þ j( f þ f ) @ @ H ¼ 2w 11 ¼ z w¼ @z @z g þ g þ j(h þ h) e e þ j( f f ) @ @ @ T z w ¼ ¼0 12 ¼ w H z ¼ @z @z @z g g þ j(h h) The same result can be obtained by the chain rule of differentiation and Eqs. (16.3.16) – (16.3.18). Example 16.3.6 We take an example of minimizing a scalar quadratic error surface with respect to a complex weight vector a p called adaptive coefficients and finding the optimum value of ap. The quadratic error surface is given by H H E p ¼ s2 þ a H p R p ap þ r p ap þ a p r p
(16:3:19)
We differentiate with respect to a p and set the result to zero and solve for a po using Eqs. (16.3.16) – (16.3.18): @ @ H H Ep ¼ ½s2 þ aH p Rp ap þ rp ap þ ap rp ¼ 0 @ap @ap ¼
@ 2 @ H @ H @ H s þ a R p ap þ r ap þ a rp ¼ 0 @a p @a p p @ap p @ap p
(16:3:20)
¼ 0 þ 2Rp ap þ 2rp ¼ 0, or apo ¼ R1 p rp The solution a po ¼ R1 p rp is called the Wiener solution for the adaptive coefficients a p. Summary of Some Vector Partial Derivatives of Real and Complex Quadratic Forms @ @ @ T @ T J(x) ¼ ½xT Ax ¼ x Ax þ x AT x ¼ ½A þ AT x : x real @x @x @x @x @ @ @ H @ T J(x) ¼ ½xH Ax ¼ x Ax þ x AT x ¼ 2Ax þ 0 : x complex @x @x @x @x (16:3:21) @ @ @ T J(x, y) ¼ ½xT Ay ¼ x Ay ¼ Ay : x real @x @x @x @ @ H @ H J(x, y) ¼ ½x Ay ¼ x Ay ¼ 2Ay : x complex @x @x @x
308
Elements of Matrix Algebra
A final point to note in taking the vector partial derivatives is that if x is real, then (@=@x)xT ¼ I, and if x is complex, then (@=@x)xH ¼ 2I and (@=@x)xT ¼ 0. Differentiation with Respect to Real Matrices
In some minimization problems there is a need to differentiate a scalar function of a real matrix A with repect to the real matrix A. We will use the following result in Section 22.3. If B is any square matrix and A is any other matrix, then @ Tr½ABAT ¼ A½B þ BT @A
(16:3:22)
@ Tr½ABAT ¼ 2AB @A
(16:3:23)
If B is symmetric, then
Example 16.3.7
We are given the matrices X and B as x11 x12 b11 b12 X¼ ; B¼ x21 x22 b21 b22
We will show that Eq. (16.3.22) is true. The trace of XBX T is given by Tr½XBXT ¼ x211 b11 þ x222 b22 þ x212 b11 þ x221 b22 þ x11 x21 b12 þ x11 x21 b21 þ x22 x12 b12 þ x22 x12 b21 Taking partial derivatives with respect to x11, x22, x12, and x21, we obtain @ Tr½XBXT ¼ 2x11 b11 þ x21 (b21 þ b12 ) @x11 @ Tr½XBXT ¼ 2x22 b22 þ x12 (b21 þ b12 ) @x22 @ Tr½XBXT ¼ 2x12 b11 þ x22 (b21 þ b12 ) @x12 @ Tr½XBXT ¼ 2x21 b22 þ x11 (b21 þ b12 ) @x21 b11 x11 x12 b11 b12 þ ¼ x21 x22 b21 b22 b12
B 16.4
b21 b22
¼ X{B þ BT }
BLOCK MATRICES
In some applications we will encounter block matrices whose elements are also matrices. An (n m) (n m) matrix Z can be formulated as a block matrix consisting of
16.4
individual blocks A,B,C,D, as 2 x11 6 6 6 6 xn1 6 Z¼6 6 6 xnþ1,1 6 6 4 xnþm,1 2 A 6 6nn ¼6 6 C 4 mn
Block Matrices
309
shown below: x1n
x1, nþ1
xn,n
xn, nþ1
B
xnþ1,n
xnþ1, nþ1
A C
x1, nþm
3
7 7 7 xn, nþm 7 7 7 7 xnþ1, nþm 7 7 7 D 5 xnþm, nþm
xnþm,n xnþm, nþ1 3 B 7 nm7 7 D 7 5 mm
(16:4:1)
We can enumerate some properties of block matrices that may be useful in later chapters: 1. Multiplication. The compatibility of multiplying blocks must be satisfied as shown: 2 3 2 3 F E A B 6 7 6 6nn nm7 nm7 6 76 n n 7 4 C D 56 H 7 4 G 5 mm mn mm mn 2 3 AE þ BG AF þ BH 6 nn nm 7 6 7 6 7 (16:4:2) ¼6 CF þ DH 7 4 CE þ DG 5 mm mn 2. Determinant. If A is nonsingular and D is square, then " # A B det ¼ det A det(D CA1 B) C D
(16:4:3)
3. Inverse. a. Matrix Inversion Lemma. If A and D are nonsingular n n and m m matrices respectively, then, (A þ BD1 C)1 ¼ A1 A1 B(D þ CA1 B)1 CA1
(16:4:4)
b. If inverses for A and D exist then,
A C
0 D
A 0
B D
"
1 ¼
"
1 ¼
A1
0
D1 CA1
D1
A1 0
A1 BD1 D1
# ; #
(16:4:5)
310
Elements of Matrix Algebra
c. If inverses for A and D exist then, 1 " D1 A B A ¼ 1 C D D1 D CA
A1 BD1 D D1 D
# (16:4:6)
where D A and D D are the Schur components of the matrices A and D defined by DA ¼ A BD1 C DD ¼ D CA1 B
(16:4:7)
d. Using property 3a, the inverses of the Schur component D A and D D are given by 1 1 þ A1 BD1 D1 A ¼A D CA 1 1 D1 þ D1 CD1 D ¼D A BD
(16:4:8)
1 We can substitute for either D1 A or DD from Eq. (16.4.8) and solve Eq. (16.4.6).
CHAPTER
17
Random Vectors and Mean-Square Estimation
B 17.1
DISTRIBUTIONS AND DENSITIES
Joint Distributions and Densities
We have come across sequences of random variables in Chapter 14, and these can be represented by a random n-vectors. We can define two random vectors X and Y as 2 3 2 3 Y1 X1 6X 7 6Y 7 6 27 6 27 7 6 7 (17:1:1) X¼6 6 .. 7; Y ¼ 6 .. 7 4 . 5 4 . 5 Xn
Ym
The joint distribution of the random vector X is given by FX (X) ¼ P½X x ¼ P½X1 x1 , . . . , Xn xn
(17:1:2)
with FX(1) ¼ 1 and FX(21) ¼ 0. The corresponding joint density function fX(x) can be defined as P(x , X x þ DxÞ DX!0 Dx
fX (x) ¼ lim ¼ lim
Dx1 !0 ... Dxn !0
Pðx1 , X1 x1 þ Dx1 , . . . , xn , Xn xn þ Dxn Þ Dx1 Dxn
(17:1:3)
which is the nth partial derivative of the distribution function FX(x): fX (x) ¼
@n FX (x) @x1 @xn
(17:1:4)
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
311
312
Random Vectors and Mean-Square Estimation
Given the density function fX(x), the distribution function FX(x) can be obtained by an n-fold integration: ð xn ð x1 FX (x) ¼ fX (x0 )dx01 dx0n (17:1:5) 1
1
T Given an n-vector X ¼ X1 Xn T , the joint distribution of X and Y is given by
Joint Distribution of Two Vectors X and Y
and an m-vector Y ¼ Y1 Ym
FXY (x,y) ¼ PðX x, Y yÞ
(17:1:6)
and the joint density, by Pðx , X x þ Dx, y , Y y þ DyÞ Dx Dy
fXY (x,y) ¼ lim
Dx!0 Dy!0
x1 , X1 x1 þ Dx1 , . . . , xn , Xn xn þ Dxn
!
P ¼ lim
Dx1 !0
.. .
y1 , Y1 y1 þ Dy1 , . . . , ym , Ym ym þ Dym Dx1 Dxn Dy1 Dym
Dxn !0
.. .
Dy1 !0
.. .
Dym !0
or fXY (x,y) ¼
@n @m FXY (x,y) @x1 @xn @y1 @ym
(17:1:7)
The distribution function FXY(x,y) can also be obtained by integrating Eq. (17.1.7): ðx ðy fxy (x0 ,y0 )dy0 dx0 (17:1:8) FXY (x,y) ¼ 1
1
Since Eq. (17.1.8) is a distribution function, we infer the following relationships: FXY (x,1) ¼ FXY (1, y) ¼ 0 FXY (x,þ1) ¼ FX (x), marginal distribution of X FXY (þ1,y) ¼ FY (y), marginal distribution of Y
(17:1:9)
FXY (þ1,þ1) ¼ 1 The marginal density functions are obtained from Eq. (17.1.7) by integration as follows: ð1 fX (x) ¼
ð1 fXY (x,y)dy1 dym
1 ð1
fY (y) ¼
1 ð1
fXY (x,y)dx1 dxn
1
(17:1:10)
1
17.1
Distributions and Densities
313
T Example 17.1.1 Thejoint density T function of a random 2-vector X ¼ X1 X2 and a random 2-vector Y ¼ Y1 Y2 is given by 8 < 0 , x1 1, 0 , x2 2 1 1 , y1 2, 1 , y2 3 fXY (x,y) ¼ (x1 þ 2x2 )(4y1 þ y2 ) , : 80 0 otherwise We have to determine (1) marginal density fX(x), (2) marginal density fY(y), (3) marginal distribution FX(x), (4) marginal distribution FY(y), (5) the distribution FX(x1), and (6) the distribution FX(x2). 1. The marginal density fX(x) is obtained by integrating fXY(x,y) over all y: ð3 ð2 1 1 fX (x) ¼ ½(x1 þ 2x2 )(4y1 þ y2 )dy1 dy2 ¼ (x1 þ 2x2 ) 80 5 1 1 2. The marginal density fY(y) is obtained by integrating fXY(x,y) over all x: ð2 ð1 1 1 fY (y) ¼ ½(x1 þ 2x2 )(4y1 þ y2 )dx1 dx2 ¼ (4y1 þ y2 ) 16 0 0 80 3. The marginal distribution FX(x) is obtained by integrating fX(x) as shown below: ð x2 ð x 1 1 1 2 (j1 þ 2j2 )dj1 dj2 ¼ x1 x2 þ 2x1 x22 FX (x) ¼ 10 0 0 5 4. The marginal distribution FY(y) is obtained by integrating fY(y) as shown below: ð y2 ð y1 1 (4h1 þ h2 )dh1 dh2 FY (y) ¼ 1 1 16 1 4( y2 1Þy21 þ ( y22 1)y1 y22 4y2 þ 5 ¼ 32 5. The distribution FX(x1) is obtained by substituting the end value x2 ¼ 2 in FX(x): 1 2 1 2 x1 x2 þ 2x1 x2 FX (x1 ) ¼ ¼ x21 þ 4x1 10 5 x2 ¼2 6. The distribution FX(x2) is obtained by substituting the end value x1 ¼ 1 in FX(x): 1 2 1 2 x1 x2 þ 2x1 x22 2x2 þ x2 FX (x2 ) ¼ ¼ 10 10 x1 ¼1 The random vectors X and Y are independent since fXY(x,y) ¼ fX(x) . fY(y), but the random variables X1 and X2 are not independent since FX(x1, x2) = FX(x1) . FX(x2).
Conditional Distributions and Densities
The conditional distribution function FXjA (xjA) given that an event A has occurred is defined by FX j A (x j A) ¼ P(X x j A) ¼
P(X x, A) , P(A) = 0 P(A)
(17:1:11)
314
Random Vectors and Mean-Square Estimation
Analogous to the one-dimensional case, we can also define a total probability. If there are disjoint events fAi, i ¼ 1, . . . , ng such that n [
Ai ¼ S, and
\
Ai Aj ¼ 1
i=j
i¼0
then the total probability is given by FX (x) ¼ P(X x) ¼
n X
P(X x j Ai )P(Ai ) ¼
i¼1
n X
FX j Ai (x j Ai )P(Ai )
(17:1:12)
i¼1
The density functions corresponding to Eqs. (17.1.11) and (17.1.12) are given by @n FX j A (x j A) , P(A) = 0 @x1 @xn n X fX j Ai (x j Ai )P(Ai ) fX (x) ¼
fX j A (x j A) ¼
(17:1:13) (17:1:14)
i¼1
If there are events A and B defined by A: x , X x þ Dx and B : y , Y y þ Dy, then the probability of B occurring conditioned on A is given by P(B j A) ¼ P(y , Y y þ Dy j x , X x þ Dx) ¼ fY j X (y j X)Dy ¼
P(y , Y y þ Dy, x , X x þ Dx) P(x , X x þ Dx)
¼
fXY (x,y)Dx Dy fXY (x,y)Dy ¼ fX (x)Dx fX (x)
(17:1:15)
From Eq. (17.1.15) we can conclude that the conditional density fY j X (y j X) is given by fY j X (y j X) ¼
fXY (x,y) fX (x)
(17:1:16)
The corresponding expression for the conditional distribution FY j X (Y j X x) is FY j X (y j X x) ¼
P(Y y, X x) FYX (y,x) ¼ P(X x) FX (x)
(17:1:17)
Example 17.1.2 In Example 17.1.1 we established that the random variables X and Y are independent and the random variables X1 and X2 are not independent. We will now find the conditional density fX2 j X1 (x2 j X1 ¼ x1 ). By Eq. (17.1.16), we have fX2 j X1 (x2 j X1 ¼ x1 ) ¼
fX2 X1 (x2 , x1 ) fX1 (x1 )
(x1 þ 2x2 ) x1 þ 2x2 5 ¼ ¼ 2(x1 þ 2) 2(x1 þ 2) 5 and the corresponding conditional distribution FX2 j X1 (x2 j X1 ¼ x1 ) is obtained by integrating the conditional density and is given by ð x2 x1 þ 2j2 x2 (x1 þ x2 ) FX2 j X1 (x2 j X1 ¼ x1 ) ¼ dj2 ¼ 2(x1 þ 2) 2(x þ 2) 1 0
17.1
Distributions and Densities
315
Note that when the end value of x2 ¼ 2 is substituted in the distribution shown above, we obtain FX2 j X1 (2 j X1 ¼ x1 ) ¼ 1, as we should. We can also find the conditional distribution FX2 j X1 (x2 j X1 x1 ) as follows: FX2 j X1 (x2 j X1 x1 ) ¼
P(X2 x2 , X1 x1 ) FX2 X1 (x2 , x1 ) ¼ P(X1 x1 ) FX1 (x1 )
1 2 (x1 x2 þ 2x1 x22 ) x (x þ 2x ) 2 1 2 10 ¼ ¼ 1 2 2(x1 þ 4) (x þ 4x1 ) 5 1 Here also if the end value of x2 ¼ 2 is substituted in the preceding distribution, we obtain FX2 j X1 (2 j X1 x1 ) ¼ 1, as we should. By differentiating the preceding equation with respect to x2, we can find the corresponding conditional density function fX2 j X1 (x2 j X1 x1 ). fX2 j X1 (x2 j X1 x1 ) ¼
d x2 (x1 þ 2x2 ) x1 þ 4x2 ¼ dx2 2(x1 þ 4) 2(x1 þ 4)
The difference between these two conditional distributions is to be particularly noted. In the first case the conditioning event is fX1 ¼ x1g, and in the second case it is fX1 x1g.
Points of Clarity
In Eq. (8.2.7) we noted some points of clarity regarding conditional densities and distributions. We will elaborate on this point. The joint density function fXY(x,y) is defined in Eq. (9.2.3) but can also be defined as an infinitesimal probability fXY (x,y)Dx Dy ¼ P(x , X x þ Dx, y , Y y þ Dy)
(17:1:18)
and the corresponding distribution function FXY(x,y), by FXY (x,y) ¼ P(X x, Y y) which can also be obtained by integrating fXY(x,y) over x and y: ðx ðy FXY (x,y) ¼ fXY (j,h)dh dj
(9:2:2)
(17:1:19)
1 1
The function fXY(x,y) can also be obtained by differentiating FXY(x,y) as given in Eq. (9.2.4): fXY (x,y) ¼
@2 FXY (x,y) @x @y
(9:2:4)
There is a need for defining another density – distribution function fXY (X x,y) that can be used in formulating directly conditional density problems involving the conditioning event such as fX x): fXY (X x,y) ¼ lim
Dy!0
P(X x,y , Y y þ Dy) Dy
(17:1:20)
316
Random Vectors and Mean-Square Estimation
Equation (17.1.20) can be obtained by integrating fXY(x,y) over x as shown below: ðx fXY (j,y)dj (17:1:21) fXY (X x,y) ¼ 1
We can integrate Eq. (17.1.21) over y and obtain FXY (x,y): ðy FXY (x,y) ¼ fXY (X x,h)dh
(17:1:22)
1
The conditonal density fY j X (y j X x) can be determined as follows: fY j X (y j X x)Dy ¼ P(y , Y y þ Dy) j X x) ¼
P(y , Y y þ Dy, X x) P(X x)
¼
fYX (y, X x)Dy FX (x)
Hence fY j X (y j X x) ¼
fYX (y, X x) FX (x)
(17:1:23)
Example 17.1.3 We will continue the previous example (Example 17.1.2) and find the conditional density fX2 j X1 (x2 j X1 x1 ). From Eq. (17.1.23) we have fX2 j X1 (x2 j X1 x1 ) ¼
fX2 X1 (x2 , X1 x1 ) FX1 (x1 )
From Eq. (17.1.21) we have ð x1 j1 þ 2x2 x1 (x1 þ 4x2 ) dj1 ¼ fX2 X1 (x2 ,X1 x1 ) ¼ 10 5 0
and
FX1 (x1 ) ¼
x21 þ 4x1 5
Hence x1 (x1 þ 4x2 ) x1 þ 4x2 fX2 j X1 (x2 j X1 x1 ) ¼ 2 10 ¼ 2(x1 þ 4) x1 þ 4x1 5 By integrating fX2 j X1 (x2 j X1 x1 ) with respect to x2, we obtain the conditional distribution FX2 j X1 (x2 j X1 x1 ) ¼
x2 (x1 þ 2x2 ) 2(x1 þ 4)
Bayes’ Theorem
Similar to the one-dimensional case, we can formulate a vector form of Bayes’ theorem given that the event Ai has occurred. We want to find the a posteriori probability P(Ai j X x) given that the event fX xg has occurred. This probability is given by P(Ai j X x) ¼
P(X x, Ai ) P(X x j Ai )P(Ai ) ¼ P(X x) P(X x)
17.1
Distributions and Densities
317
Substituting for P(X x) from Eq. (17.1.12), we obtain the vector form of Bayes’ theorem: P(X x j Ai )P(Ai ) P(Ai j X x) ¼ Pn k¼1 P(X x j Ak )P(Ak )
(17:1:24)
We can also formulate Bayes’ rule for random vector densities. If fXY(x,y) represents the joint density associated with random vectors X and Y, then, from Eq. (17.1.16), we obtain fXY (x,y) ¼ fY j X (y j X ¼ x)fX (x) and Bayes’ theorem can be formulated for fX j Y(x j Y ¼ y) as follows: fX j Y (x j Y ¼ y) ¼
fY j X (y j X ¼ x)fX (x) fY (y) fY j X (y j X ¼ x)fX (x) 0 0 0 1 fY j X (y j X ¼ x )fX (x )dx
¼ Ð1
(17:1:25)
In many engineering estimation problems the random vector X cannot be observed but the random vector Y connected to X can be observed. In this connection fX(x) is called the a priori density and fX j Y(x j Y ¼ y) is called the a posteriori density after observing fY ¼ yg.
The density fX(x) of a two-dimensional random vector X is given by
Example 17.1.4
fX (x) ¼
4x1 þ 7x2 , 0 , x1 1, 0 , x2 2 18
The conditional density fY j X(y j x) of another two-dimensional random vector Y is fY j X (y j x) ¼
4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 , 5(4x1 þ 7x2 )
0 , x1 1, 0 , x2 2 1 , y1 2, 1 , y2 3
Given this information, we have to find the a posteriori density fX j Y(x j y) using Bayes’ theorem of Eq. (17.1.24). The joint density function fYX(x,y) is given by fYX (y,x) ¼ fY j X (y j x)fX (x) 4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 4x1 þ 7x2 5(4x1 þ 7x2 ) 18 0 , x1 1, 0 , x2 2 1 ¼ (4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 ), 90 1 , y1 2, 1 , y2 3
¼
The marginal density function fY(y) is obtained by integrating the preceding equation with respect to x:
1 7y1 þ 6y2 4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 dx2 dx1 ¼ 45 0 90
ð1 ð2 fY (y) ¼ 0
318
Random Vectors and Mean-Square Estimation
The a posteriori density can be obtained from Eq. (17.1.25) as fX j Y (x j y) ¼
fY j X (y j x)fX (x) fY (y)
4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 4x1 þ 7x2 5(4x1 þ 7x2 ) 18 ¼ 7y1 þ 6y2 45 0 , x1 , 1, 0 , x2 2 4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 , ¼ 2(7y1 þ 6y2 ) 1 , y1 2, 1 , y2 3 which, when integrated over all x, indeed gives the result 1. Example 17.1.5
The density fX(x) of a two-dimensional vector X is given by fX (x) ¼ a1 d(x1 m1 , x2 m2 ) þ a2 d(x1 m1 , x2 m2 )
where a1 þ a2 ¼ 1, d(x1,x2) is a two-dimensional Dirac delta function and m1,m2 are constants. The conditional density of a two-dimensional random vector Y correlated with X is 1 (y1 x1 )2 þ (y2 x2 )2 e fY j X (y j x) ¼ p From Eq. (17.1.6) the joint density function fYX(x,y) is given by fYX (y,x) ¼ fY j X (y j x)fX (x)
1 (y1 x1 )2 þ(y2 x2 )2 e a1 d(x1 m1 , x2 m2 ) þ a2 d(x1 m1 , x2 m2 ) ¼ p
1 (y1 m1 )2 þ(y2 m2 )2 ¼ e a1 d(x1 m1 , x2 m2 ) þ a2 d(x1 m1 , x2 m2 ) p The marginal density function fY(y) is obtained by integrating this equation with respect to x: ð1 ð1 a1 d(x1 m1 , x2 m2 ) 1 (y1 m1 )2 þ(y2 m2 )2 e fY (y) ¼ dx1 dx2 þa2 d(x1 m1 , x2 m2 ) 1 1 p 1 (y1 m1 )2 þ(y2 m2 )2 (y1 m1 )2 þ(y2 m2 )2 a1 e þ a2 e ¼ p The a posteriori density can be obtained from Eq. (17.1.25) as fy j x (y j x)fX (x) fY (y)
(y1 m1 )2 þ(y2 m2 )2 a1 d(x1 m1 , x2 m2 ) þ a2 d(x1 m1 , x2 m2 ) e ¼ 2 2 2 2 a1 e (y1 m1 ) þ(y2 m2 ) þ a2 e (y1 m1 ) þ(y2 m2 )
fx j y (x j y) ¼
¼ b1 (y)d(x1 m1 , x2 m2 ) þ b2 (y)d(x1 m1 , x2 m2 ) where 2
2
ai e½(y1 m1 ) þ(y2 m2 ) , bi (y) ¼ 2 2 2 2 a1 e (y1 m1 ) þ(y2 m2 ) þ a2 e (y1 m1 ) þ(y2 m2 )
i ¼ 1,2
17.2
B 17.2
Moments of Random Vectors
319
MOMENTS OF RANDOM VECTORS
Expectation Vector
Analogous to the single-dimensional case, the expected value of a random n-vector X is another n-vector mX defined by ð1 ð1 x f X (x)dx1 dxn (17:2:1) E½X ¼ mX ¼ 1
1
and ð1 E½Xi ¼ mXi ¼ ð1
ð1 ...
1
xi f X (x)dx1 dxi dxn 1
xi f Xi (xi )dxi
¼
(17:2:2)
1
since ð1
ð1 fX (x)dx1 dxi1 dxiþ1 dxn ¼ 1
1
1
Hence the mean value n-vector mX can be written as T mX ¼ mX1 mXi mXn
(17:2:3)
The term X is a 3-vector whose pdf fX(x) is given by 2 2x 3 2e 1 4 fX (x) ¼ 3e3x25, x1 ,x2 ,x3 . 0 4e4x3
Example 17.2.1
The mean value vector mX is: 2 3 1 2 3 627 E(X1 ) 6 7 617 mX ¼ 4E(X2 )5 ¼ 6 7 637 E(X3 ) 415 4 Correlation Matrix
The correlation matrix is a square matrix RX representing the set of all second moments of the components of a real random vector X of the form ð1 xi xj fXi Xj (xi , xj )dxi dxj , i = j, i, j ¼ 1, . . . , n (17:2:4) E½Xi X j ¼ 1
and is defined by 2
E½X12
E½X1 X2
6 6 E½X2 X1 E½X22 E½XX ¼ RX ¼ 6 6 .. .. 4 . . E½Xn X1 E½Xn X2 T
E½X1 Xn
3
7 E½X2 Xn 7 7 7 .. 5 . 2 E½Xn
(17:2:5)
320
Random Vectors and Mean-Square Estimation
The quantity E[XX T] is defined as the outer product of the vector X, which is equal to the P correlation matrix RX. The inner product RX is defined by E½XT X ¼ ni¼1 E½Xi2 , which is equal to the trace of RX or E[X TX] ¼ Tr[RX]. The expected value of a scalar function g(X) of a vector random variable X can be defined as follows: ð1 ð1 ð1 E½g(X) ¼ g(X)fX (x)dx ¼ g(X)fX (x)dx1 dxn (17:2:6) 1
1
1
Example 17.2.2 The correlation matrix of Example 17.2.1 can be obtained from Eq. 17.2.4 assuming that X1, X2,X3 are independent and hence the joint densities fXi Xj (xi , xj ) are given by the product of individual densities as shown below: fXi Xj (xi , xj ) ¼ fXi (xi )fXj (xj ), i = j, i, j ¼ 1,2,3 or fX1 X2 (x1 , x2 ) ¼ 6e(2x1 þ3x2 ) u(x1 )u(x2 ) fX2 X3 (x2 , x3 ) ¼ 12e(3x2 þ4x3 ) u(x2 )u(x3 ) fX1 X3 (x1 , x3 ) ¼ 8e(2x1 þ4x3 ) u(x1 )u(x3 ) Hence RX is given by 2
E½X12 6 RX ¼ 4 E½X2 X1 E½X3 X1
E½X1 X2 E½X22 E½X3 X2
2
1 6 E½X1 X3 62 7 61 E½X2 X3 5 ¼ 6 66 41 E½X32 8 3
3 1 1 6 8 7 7 2 1 7 7 9 12 7 1 1 5 12 8
The trace of the matrix Tr(Rx Þ ¼ 12 þ 29 þ 18 ¼ 61 72. We note that the correlation matrix is symmetric. Covariance Matrix
The covariance matrix CX associated with a real random value of the outer product of the vector (X 2 mX) and is given 2 2 sX1 sX1 X2 sX1 Xj 6 6 sX2 X1 s2X2 sX2 Xj 6 6 . .. .. 6 .. . . T CX ¼ E½X mX ½X mX ¼ 6 6s 6 Xi X1 sXi X2 sXi Xj 6 .. .. 6 .. 4 . . . sXn X1 sXn X2 sXn Xj
vector X is the expected by 3 sX 1 X n 7 sX 2 X n 7 7 .. 7 . 7 7 (17:2:7) sXi Xn 7 7 7 .. 7 . 5 s2Xn
where sXi Xj is the covariance between the random variables Xi and Xj given by sXi Xj ¼ E(Xi mXi )(Xj mXj )
(17:2:8)
Equation (17.2.7) can be expanded to express the covariance matrix CX in terms of the correlation matrix RX: CX ¼ E XXT E X mTX mX E XT þ mX mTX ¼ E XXT mX mTX ¼ RX mX mTX (17:2:9)
17.2
Moments of Random Vectors
321
Example 17.2.3 We will find the covariance matrix for Examples 17.2.1 and 17.2.2. The correlation matrix RX has already been found. The matrix mX mTX is given by 2 3 1 2 3 E(X1 ) 6 2 7 7 6 7 6 617 1 1 1 6 7 T mX mX ¼ 6 E(X2 ) 7 E(X1 ) E(X2 ) E(X3 ) ¼ 6 7 637 2 3 4 4 5 415 E(X3 ) 4 2 3 1 1 1 64 6 8 7 6 7 1 7 61 1 ¼6 7 6 6 9 12 7 41 1 1 5 8 12 16 From Eq. (17.2.9) the covariance matrix can be given by 2 3 2 1 1 1 1 1 62 6 6 7 8 7 64 6 6 1 7 61 1 61 2 T CX ¼ RX mX mX ¼ 6 76 6 6 9 12 7 6 6 9 41 1 1 5 41 1 8 12 8 8 12
3 2 1 1 6 7 8 7 64 1 7 6 7 ¼ 60 12 7 6 1 5 4 0 16
0 1 9 0
0
3
7 7 7 0 7 7 1 5 16
Since the cross-terms are zero, the random variables X1,X2, and X3 are pairwise uncorrelated. Joint Moments of Two Random Vectors
Given a random n-vector X and m-vector Y, with joint density fXY(x,y), the expectation of a scalar function g(X,Y) of the random vectors X,Y is defined by ð1 ð1 g(x,y)fXY (x,y)dx dy E½g(X,Y) ¼ 1
1
1
1
ð1 ð1
g(x,y)fXY (x,y)dx1 dxn dy1 dym
¼
(17:2:10)
The cross-correlation matrix RXY represents the set of all joint moments of the components of real random vectors X and Y of the form ð1 xi yj fXi Yj (xi ,yj )dxi dyj , i ¼ 1, . . . , n, j ¼ 1, . . . ,m (17:2:11) E½Xi Yj ¼ 1
and is defined by the outer product E[XY T] as follows: 2 3 E½X1 Y1 E½X1 Y2 E½X1 Ym 6 7 6 E½X2 Y1 E½X2 Y2 E½X2 Ym 7 6 7 E½XYT ¼ RXY ¼ 6 7 .. .. .. 6 7 . . . 4 5 E½Xn Y1 E½Xn Y2 E½Xn Ym
(17:2:12)
It is not a square matrix unless mP¼ n. The inner product E[X TY] is not defined unless m ¼ n, in which case E½XT Y ¼ ni¼1 E½Xi Yi .
322
Random Vectors and Mean-Square Estimation
Cross-Covariance Matrix
The cross-covariance matrix CXY associated with random n-vector X and a random m-vector Y is expected value of the outer product (X 2 mX) and (Y 2 mY) and is given by 2
CXY
sX 1 Y1 6 sX 2 Y1 6 6 . 6 .. 6 T ¼ E½X mX ½Y mY ¼ 6 6 sXi Y1 6 6 .. 4 . sX n Y1
sX 1 Y2 sX 2 Y2 .. . sX i Y2 .. . sX n Y2
sX1 Yj sX2 Yj .. . sX i Yj .. . sXn Yj
3 sX 1 Ym sX 2 Ym 7 7 .. 7 . 7 7 (17:2:13) sXi Ym 7 7 7 .. 7 . 5 sX n Ym
Again CXY is not a square matrix unless m ¼ n. If m ¼ n then Tr½CXY ¼ Sni¼1 E½Xi Yi . Similar to Eq. (17.2.9), Eq. (17.2.13) can be expanded to express the cross-covariance matrix CXY in terms of the cross-correlation matrix RXY: CXY ¼ E XYT E X mTY mX E YT þ mX mTY ¼ E XYT mX mTY ¼ RXY mX mTY
(17:2:14)
Example 17.2.4 We will now find the cross-covariance matrix of the two-dimensional vectors X and Y given the joint density
fXY (x,y) ¼
1 (4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 ), 90
0 , x1 1, 0 , x2 2 1 , y1 2, 1 , y2 3
We will first find the four densities, fX1 (x1 ), fX2 (x2 ), fY1 (y1 ), fY2 (y2 ) as follows: ð2 ð2 ð3 1
1 4x1 þ 7 (4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 )dy2 dy1 dx2 ¼ 9 1 90
1
1 7x2 þ 2 (4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 )dy2 dy1 dx1 ¼ 90 18 1
0
1 14y1 þ 24 (4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 )dy2 dx2 dx1 ¼ 90 45 1
0
1 4y1 þ 7 (4x1 y1 þ 2x1 y2 þ 5x2 y1 þ 5x2 y2 )dy1 dx2 dx1 ¼ 90 30 1
fX1 (x1 ) ¼ 0
ð1 ð2 ð3 fX2 (x2 ) ¼ 0
ð1 ð2 ð3 fY1 (y1 ) ¼ 0
ð1 ð2 ð2 fY2 (y2 ) ¼ 0
The mean vectors mX,mY and the outer product mXmTY can be obtained from the densities as follows: 2
3 29 6 54 7 7 mX ¼ 6 4 5 5, 27
2
3 134 6 45 7 7 mY ¼ 6 4 119 5, 180
2
3 1943 3451 6 1215 9720 7 7 mX mTY ¼ 6 4 134 119 5 243 972
17.3
Vector Gaussian Random Variables
323
To find the cross-correlation matrix RXY, we need the joint densities fX1 Y1 (x1 ,y1 ), fX1 Y2 (x1 ,y2 ), fX2 Y1 (x2 ,y1 ), fX2 Y2 (x2 ,y2 ). They can be determined from fXY(x,y) and are shown below: 8x1 y1 þ 8x1 þ 10y1 þ 20 45 4x1 y2 þ 12x1 þ 10y2 þ 15 fX1 Y2 (x1 ,y2 ) ¼ 90 5x2 y1 þ 10x2 þ 2y1 þ 2 fX2 Y1 (x2 ,y1 ) ¼ 45 10x2 y2 þ 15x2 þ 2y2 þ 6 fX2 Y2 (x2 ,y2 ) ¼ 180 fX1 Y1 (x1 ,y1 ) ¼
We can now find the various joint expectations as follows: 332 405 778 E½X2 Y1 ¼ 405 E½X1 Y1 ¼
454 405 , 1066 E½X2 Y2 ¼ 405
2
E½X1 Y2 ¼
hence
RXY
3 454 405 7 7 1066 5 405
332 6 405 ¼6 4 778 405
From Eq. (17.2.14) we can evaluate CXY ¼ RXY mX mTY : 2
CXY
3 2 332 454 1943 6 405 405 7 6 1215 7 6 ¼6 4 778 1066 5 4 134 243 405 405 1:516 0:766 ¼ 1:370 2:510
3 2 163,963 3451 6 108,135 9720 7 7¼6 119 5 4 1664 972 1215
3 1489 1944 7 7 12,197 5 4860
Definitions 1. Two random vectors X and Y are independent if fXY(x,y) ¼ fX(x) fY(y). 2. Two random vectors X and Y are uncorrelated if RXY ¼ mXmTY, or CXY ¼ 0. 3. Two random vectors X and Y are orthogonal if RXY ¼ 0. The definitions and properties of correlation and covariance matrices for two real random vectors X and Y are shown in Table 17.2.1.
B 17.3
VECTOR GAUSSIAN RANDOM VARIABLES
Gaussian random variables play an important role in signal processing and communication problems. For a real n-vector random variable, the multivariate Gaussian density takes the form fX (X) ¼
1 e(1=2) (x mx )T C1 X (x mx ) (2p)n=2 j CX j1=2
(17:3:1)
324
Random Vectors and Mean-Square Estimation
TABLE 17.2.1 Property
Correlation functions
Covariance functions
Autocorrelation and Autocovariance RX ¼ E[XX T] RX ¼ RTX Positive definite Greater than 0 Orthogonal Greater than 0 RX ¼ CX þ mXmTX
Definition Symmetry Definiteness Eigenvalues Eigenvectors Principal diagonals Relationship
CX ¼ E[(X 2 mX) (X 2 mX)T] CX ¼ CTX Positive definite Greater than 0 Orthogonal Greater than 0 CX ¼ RX 2 mXmTX
Cross-Correlation and Cross-Covariance RXY ¼ E[XY T] RXY ¼ RTYX Nondefinite RXY ¼ CXY þ mXmTY RXY ¼ mXmTY RXY ¼ 0 ¼ RYX RXþY ¼ RX þ RXY þ RYX þ RY RXþY ¼ RX þ RY if X, Y are orthogonal
Definition Symmetry Definiteness Relationship Uncorrelated Orthogonal Sum of X and Y
CXY ¼ E[(X 2 mX) (Y 2 mY)T] CXY ¼ CTYX Nondefinite CXY ¼ RXY 2 mXmTY CXY ¼ 0 ¼ CYX CXþY ¼ CX þ CXY þ CYX þ CY CXþY ¼ CX þ CY if X, Y are uncorrelated
The joint density of two random variables X and Y is given by a bivariate Gaussian as in Eq. (10.5.1) ( "
1 1 x mx 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi exp fXY (x,y) ¼ 2(1 r2 ) sx 2psx sy 1 r2 #)
(x mx )(y my ) y my 2 2r þ sy s x sy
(10:5:1)
where r is the correlation coefficient between random variables X and Y given by r ¼ sxy =(sx sy ). This can also be expressed in the matrix form of Eq. (17.3.1) with n ¼ 2 ( 1 1 ½x mx fXY (x,y) ¼ pffiffiffiffiffiffiffiffiffi exp 2 2p jCX j
s2 y my X rsX sY
rsX sY s2Y
1
x mx y my
)!
(17:3:2) where the covariance matrix CX, its inverse CX 21, and the mean vector m are given by " CX ¼
s2X rsX sY
rsX sY s2Y
2
# ,
3 1 r 6 s2X (1 r2 ) sX sY (1 r2 ) 7 6 7, C1 X ¼4 5 r 1 sX sY (1 r2 ) s2Y (1 r2 )
mX m¼ mY
(17:3:3)
17.3
Vector Gaussian Random Variables
325
2 In a similar fashion, when n ¼ 1, CX ¼ s2X, C21 X ¼ 1/sX, r ¼ 0, and the density for a single-dimensional random variable X from Eq. (17.3.1) is 2 1 fX (x) ¼ pffiffiffiffiffiffiffiffiffiffiffi e(1=2)½(xmX )=sX 2psX
(17:3:4)
a result that has already been stated in Eq. (6.4.1). If random n-vector X and m-vector Y are Gaussian-distributed, then the joint density fXY(x,y) can be obtained by forming an (n þ m) vector
X Z¼ Y
(17:3:5)
with the corresponding mean vector and the covariance matrix given by mZ ¼ E
X
¼
mX
mY X mX CX (X mX )T (Y mY )T ¼ CZ ¼ E Y mY CYX Y
CXY
(17:3:6)
CY
Since CZ is block symmetric, we note that CTYX ¼ CXY. The joint density fZ(z) ¼ fXY(x,y) can be obtained from Eq. (17.3.1) as fXY (x,y) ¼
T 1 1 e(1=2)(zmZ ) CZ (zmZ ) (2p)ðnþmÞ=2 j CZ j1=2
(17:3:7)
where the determinant jCZj is obtained from Eq. (16.4.3) jCZ j ¼ jCX j j CY CYX C1 X CXY j
(17:3:8)
and the inverse C1 Z , from Eq. (16.4.6) CX CXY 1 CYX CY 2 1 1 1 CX þ C1 X CXY DCY CYX CX 4 ¼ 1 D1 CY CYX CX
C1 Z ¼
1 C1 X CXY DCY
D1 CY
3 5
(17:3:9)
where the inverse of the Schur component D1 CY of CY is obtained from Eq. (16.4.7): 1 1 D1 CY ¼ (CY CYX CX CXY )
It can be seen from Eq. (17.3.7) that when the densities fX(x) and fY(y) are jointly Gaussian, the density fXY(x,y) is also jointly Gaussian.
326
Random Vectors and Mean-Square Estimation
The conditional density fY j X(y j x) is obtained from fY j X(y j x) ¼ fXY(x,y)/ fX(x). Performing the indicated division and after some involved matrix manipulation, we obtain fY j X (y j x) ¼
m=2
(2p)
T 1 1 e(1=2)(ymY j X ) CY j X (ymY j X ) 1=2 j CY j X j
(17:3:10)
1 where mY j X ¼ mY þ CYX C1 X (x mX ) and CY j X ¼ CY CYX CX CXY . We note that the conditional density is also Gaussian.
Example 17.3.1 Two-dimensional zero mean Gaussian random vectors X and Y have covariance matrices equal to
1 CX ¼ 0
0 9
4 CY ¼ 0
and
0 16
The cross-covariance matrix between X and Y is given by CXY ¼
CX1 Y1
CX1 Y2
CX2 Y1
CX2 Y2
2 6 ¼4
1 18 5
3 6 5 7 ¼ CT YX 12 5 5
We have to find the joint density fXY(x,y) and the conditional density fY j X(y j x) using Eqs. (17.3.7) and (17.3.10).
Joint Density
From the data given, we can form the matrix
CX CZ ¼ CYX
CXY CY
1 0 , CX ¼ 0 9
with
4 CY ¼ 0
0 16
and the cross-covariance matrices CXY and CYX, given by 2
1
6 CXY ¼ 6 4 18 5
3 6 5 7 7 12 5 5
2 and
1
6 CYX ¼ CTXY ¼ 6 46 5
3 18 5 7 7 12 5 5
The p ffiffiffiffiffiffiffiffiffimean vectors mX and mY are zero vectors. From Eq. (17.3.8), jCZj and jCZ j are jCZ j ¼
95904 ¼ 153:446 625
and
pffiffiffiffiffiffiffiffiffi 36 pffiffiffiffiffi jCZ j ¼ 74 ¼ 12:387 25
17.3
Vector Gaussian Random Variables
327
The inverse matrix C1 Z is obtained from Eq. (17.3.9): " C1 Z
¼
1 1 1 C1 X þ CX CXY DCY CYX CX
1 C1 X CXY DCY
1 D1 CY CYX CX
D1 CY
#
3 250 50 215 35 7 6 111 222 148 7 6 111 7 6 6 50 275 40 25 7 7 6 7 6 111 999 111 333 7 6 ¼6 7 725 75 7 6 215 40 7 6 6 222 111 888 592 7 7 6 4 35 25 75 325 5 148 333 592 3552 3 2 2:252 0:45 0:968 0:236 7 6 7 6 7 6 6 0:45 0:275 0:36 0:075 7 7 6 7 6 ¼6 7 7 6 7 6 0:968 0:36 0:816 0:127 7 6 7 6 5 4 2
0:236
0:075
0:127
0:091
With this information, the joint density fXY(x,y) is given by
fXY (x,y) ¼
1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (2p) 153:446 8 2 0 2:252 > > > B 1< 6 B 6 0:45 ½x j y6 expB @ 2> 4 0:968 > > : 0:236 2
0:45 0:275 0:36 0:075
91 > > =C 7 > 0:075 7 x C C 7 A 0:127 5 y > > > ; 0:091
0:968 0:236 0:36 0:816 0:127
3
or
fXY (x,y) ¼
1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (2p) 153:446 2 3 0:408y21 þ (0:127y2 þ 0:968x1 þ 0:36x2 )y1 6 7 6 7 exp6 0:045y22 þ (0:236x1 þ 0:075x2 )y2 1:126x21 7 4 5 0:45x1 x2 0:1375x22 2
328
Random Vectors and Mean-Square Estimation
Conditional Density. We will now calculate the conditional density fY j X(y j x) from Eq. (17.3.10). The conditional expectation vector mY j X is given by 0 mY j X ¼ mY þ CTXY C1 (x m ) ¼ X X 0 2 3 2 3 18 2 2x2 3 1 0 x1 þ 1 6 6 x1 5 7 5 7 74 7 ¼6 þ6 15 4 6 12 5 4 x2 6x1 4x2 5 0 þ 9 5 5 5 15 The conditional covariance matrix CY j X is obtained as follows: 2 3 2 3 18 2 1 0 61 5 7 6 1 4 0 56 74 CY j X ¼ CY CTXY C1 6 1 X CXY ¼ 4 6 12 5 4 18 0 16 0 9 5 5 5 3 2 39 54 6 25 25 7 7 ¼6 4 54 348 5 25
3 6 5 7 7 12 5 5
25
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 10656 The determinant jCY j X j ¼ ¼ 17:05 and jCY j X j ¼ 4:129 and 625 2 2 3 3 725 75 39 54 1 6 25 6 888 592 7 0:816 0:127 25 7 6 6 7 7 ¼ ¼ ¼ C1 YjX 4 54 348 5 4 75 0:127 0:091 325 5 592 3552 25 25 Substituting these values in Eq. (17.3.10), we obtain 8 0 > < 1 2x2 B 1 fY j X (y j x) ¼ pffiffiffiffiffiffiffiffiffiffiffi exp@ y1 x1 2> 5 2p 17:05 :
0:816 0:127
y1
6x1 4x2 5 15
2 391 y1 x1 2x2 > = 0:127 6 C 5 7 A 4 5 6x 4x > 1 2 0:091 ; y1 5 15
Or expanding this equation results in the conditional density fY j X(y j x) as follows: fY j X (y j x) ¼
1 pffiffiffiffiffiffiffiffiffiffiffi 2p 17:05 " # 0:408y21 þ (0:127y2 þ 0:968x1 þ 0:36x2 )y1 0:045y22 exp þ(0:236x1 þ 0:075x2 )y2 0:626x21 0:45x1 x2 0:082x22
Linear Transformations
In Section 13.5 we discussed the general case of the transformation Z ¼ g(X), where Z and X are random n-vectors and g is a function n-vector. We will now discuss a linear
17.3
Vector Gaussian Random Variables
329
transformation of the form 2
3 2 y1 a11 6 y2 7 6 a21 6 7 6 Y¼6 . 7¼6 . 4 .. 5 4 .. an1 yn
a12 a22 .. . an2
32 3 x1 a1n 6 x2 7 a2n 7 76 7 .. 76 .. 7 ¼ AX . 54 . 5 xn ann
(17:3:11)
where X is a Gaussian random n-vector and A is a square nonsingular n-matrix faijg. The density function for X is given in Eq. (17.3.1): fX (x) ¼
T 1 1 e(1=2)(xmX ) CX (xmx ) (2p)n=2 jCX j1=2
(17:3:1)
Using the transformation techniques developed in Chapter 13, the solution for X is given by X ¼ A21Y. The Jacobian determinant kJk ¼ jAj. The density function fY(y) is given by fY (y) ¼
(2p)
n=2
(2p)
n=2
(2p)
n=2
¼ ¼
1 1 T 1 1 eð1=2Þ(A ymx ) CX (A ymx ) 1=2 jAjjCX j
1 ð1=2Þ(yAmX )T (AT CX1 A1 )(yAmX ) e T 1=2 jAjjCX jjA j 1 ð1=2Þ(yAmX )T (ACX AT )1 (yAmX ) e T 1=2 jAjjCX jjA j
(17:3:12)
Taking the expectation and outer product of Eq. (17.3.11), we obtain mY ¼ AmX E½YYT ¼ CY ¼ E½AXXT AT ¼ AE½XXT AT ¼ ACX AT
(17:3:13)
Substituting Eqs. (17.3.13) into Eq. (17.3.12), we can write fY(y) as fY (y) ¼
(2p)
n=2
1 ð1=2Þ(ymY )T C1 Y (ymY ) 1=2 e jCY j
ð17:3:14Þ
Example 17.3.2 Starting with the same example (Example 13.5.1), we will solve for fY(y) using matrix methods. A zero mean Gaussian random 3-vector X has the density function fX(x) fX (x) ¼
1 T 1 e(1=2)x CX x 3=2 (2p)
where 2
1 CX ¼ 4 0 0
0 1 0
3 0 0 5, 1
2
jCX j ¼ 1,
C1 X
1 0 ¼ 40 1 0 0
3 0 05 1
330
Random Vectors and Mean-Square Estimation
The transformation Y ¼ AX is given by 2 3 2 y1 2 Y ¼ 4 y2 5 ¼ 4 5 3 y3
2 3 2
32 3 1 x1 3 54 x2 5 ¼ AX 2 x3
Taking expectation on both sides of the preceding mX ¼ 0. The covariance matrix CY is given by 2 32 32 2 2 1 1 0 0 2 CY ¼ ACX AT ¼ 4 5 3 3 54 0 1 0 54 2 3 2 2 0 0 1 1
equation we have mY ¼ 0, since 3 2 5 3 9 19 3 2 5 ¼ 4 19 43 3 2 12 27
3 12 27 5 17
The inverse of CY is 2
C1 Y
2 1 ¼4 1 9 13 15
3 3 15 5 26
and the determinant of CY ¼ 1. Hence fY(y) is obtained from Eq. (17.3.14) 8 2 2 < 1 1 T4 y 1 fY (y) ¼ exp : 2 (2p)3=2 1 13
1 9 15
3 9 3 = 15 5y ; 26
or fY (y) ¼
1 e(1=2) (2p)3=2
2y21 þ9y22 þ26y23 þ2y1 y2 30y2 y3 6y3 y1
which is exactly the same as obtained in Example 13.5.1.
B 17.4 DIAGONALIZATION OF COVARIANCE MATRICES Diagonalization Principles
In Section 16.2 we discussed diagonalization of a nonsingular square matrix by another transformation matrix of eigenvectors called the modal matrix. Since covariance matrices are symmetric and positive definite, the modal matrix is orthogonal. This property makes diagonalization of covariance matrices somewhat simpler. Diagonalization is needed to make a problem simpler, and correlated Gaussian random variables become independent. Given any positive definite covariance n-matrix C with distinct eigenvalues, we find the eigenvalue n-matrix L by solving the characteristic equation jlI 2 Cj ¼ 0 as outlined in Section 16.2. 2 3 l1 0 0 6 0 l2 0 7 6 7 L¼6 . (17:4:1) .. 7 .. 4 .. . 5 . 0 0 ln
17.4
Diagonalization of Covariance Matrices
331
We find the eigenvectors ffi, i ¼ 1, . . . , ng corresponding to each of the eigenvalues fli, i ¼ 1, . . . , ng and form the modal matrix M of eigenvectors: 2 3 f11 f21 fn1 7 6 6 f12 f22 fn2 7 (17:4:2) M ¼ f1 f2 fn ¼ 6 . 7 . . .. 5 .. 4 .. f1n f2n fnn Since C is symmetric, M will be orthogonal: 1, i ¼ j fTi fj ¼ and MT ¼ M1 0, i = j
(17:4:3)
With these definitions, the following eigenequation can be formed: CM ¼ ML
and MT CM ¼ L
or M1 CM ¼ L
(17:4:4)
The problem now is to find a transformation T between two random n-vectors X and Y such that the covariance matrix CX is diagonalized to the covariance matrix CY. We take the expected value and the covariance of Y in the transformation (17:4:5) (17:4:6)
Y ¼ TX mY ¼ TmX Thus E½(Y mY )(Y mY )T ¼ E½T(X mX )(X mX )T TT ,
or CY ¼ TCX TT (17:4:7)
Or, identifying terms in Eqs. (17.4.4) and (17.4.7), we obtain CY L, CX C,
T MT
(17:4:8)
T
with the required transformation matrix T M . Diagonalizing to an Identity Matrix
We can carry the diagonalization process one step further and find a transformation matrix D such that CY is an identityp matrix. ffiffiffiffiT Since pffiffiffiffi L is a diagonal matrix, we can take the positive square root and write L ¼ L In L ,where identity n-matrix. Incorpffiffiffiffi TIn isan pffiffiffiffi porating this result in Eq. (17.4.4) and noting that L ¼ L , we obtain pffiffiffiffi1 pffiffiffiffi pffiffiffiffi pffiffiffiffi1 MT CM ¼ or L MT CM L ¼ In L In L (17:4:9) Thus the required transformation matrix D in the transformation D TCD ¼ In is pffiffiffiffi1 D¼M L (17:4:10) The transformation T is orthogonal, whereas D is not. Example 17.4.1 given by
The covariance matrix CX of a zero mean Gaussian random 3-vector is 2
3
6 CX ¼ 4 1 0
1 0
3
7 2 15 1 3
332
Random Vectors and Mean-Square Estimation
We have to determine 1. The joint density fX(x) 2. The correlation coefficient matrix r, given by 3 2 1 rX1 X2 rX1 X3 7 6 1 rX2 X3 7 r¼6 5 4 rX 2 X 1 rX3 X1 rX3 X2 1 3. The modal matrix M and show that it is orthogonal 4. The transformation matrix T in Y ¼ TX that will make CY diagonal 5. The transformation matrix D that will make CX an identity matrix 6. The joint density fY(y) 1. With jCXj ¼ 12 and 2
C1 X
5 1 6 12 4 6 6 1 3 6 ¼6 6 4 4 6 4 1 1 12 4
3 1 12 7 7 7 1 7 7 4 7 7 5 5 12
in the equation fX (x) ¼
T 1 1 pffiffiffiffiffiffiffiffiffi e(1=2)X CX X (2p)3=2 jCX j
the joint density is given by fX (x) ¼
1 5x21 x3 x2 x2 x3 3x22 5x23 p ffiffiffiffiffi þ þ þ exp x 1 2 12 6 2 2 4 12 (2p)3=2 12 1
2. From the given covariance matrix CX, we have s2X1 ¼ 3,
s2X2 ¼ 2, s2X3 ¼ 3 and
sX1 X2 ¼ 1, sX1 X2 ¼ 0,
Hence the correlation matrix r is given by 2 3 1 1 pffiffiffi 0 6 7 6 6 7 6 1 7 1 7 p ffiffi ffi p ffiffi ffi 1 r¼6 6 6 67 6 7 4 5 1 p ffiffi ffi 0 1 6 3. From the determinant of the matrix 2 l3 jlI CX j ¼ 4 1 0
1 l2 1
3 0 1 5 ¼ 0 l3
sX2 X3 ¼ 1
17.4
Diagonalization of Covariance Matrices
333
we obtain the characteristic equation, l3 2 8l2 þ 19l 2 12 ¼ (l 2 1)(l 2 3) (l 2 4) ¼ 0, and the eigenvalues are l1 ¼ 1, l2 ¼ 3, l4 ¼ 4. The eigenvectors corresponding to these eigenvalues are obtained by the methods described in Example 16.2.5 and are shown below with the modal matrix M: 3 1 pffiffiffi 6 67 7 6 6 2 7 6 pffiffiffi 7 f1 ¼ 6 7, 6 67 7 6 4 1 5 pffiffiffi 6 2 1 1 pffiffiffi pffiffiffi 6 6 2 6 6 2 6 M ¼ 6 pffiffiffi 0 6 6 6 4 1 1 pffiffiffi pffiffiffi 6 2 2
2
3 1 pffiffiffi 6 27 6 7 6 7 f2 ¼ 6 0 7, 6 7 4 1 5 pffiffiffi 2
3 1 pffiffiffi 6 37 7 6 6 1 7 6 pffiffiffi 7 f3 ¼ 6 7, 6 37 7 6 4 1 5 pffiffiffi 3 2
3 1 pffiffiffi 37 7 1 7 pffiffiffi 7 7 37 7 1 5 pffiffiffi 3
By direct multiplication we can see that fT1 f2 ¼ 0, fT1 f3 ¼ 0, fT2 f3 ¼ 0, and fT1 f1 ¼ fT2 f2 ¼ fT3 f3 ¼ 1, thus showing orthogonality. Further, jMj ¼ 1. 4. From Eq. (14.7.8) the transformation matrix T is 2 1 pffiffiffi 6 6 6 6 1 T ¼ MT ¼ 6 6 pffiffi2ffi 6 4 1 pffiffiffi 3
2 1 3 pffiffiffi pffiffiffi 6 67 7 1 7 0 pffiffiffi 7 27 7 1 1 5 pffiffiffi pffiffiffi 3 3
2
and
1 TCX T ¼ CY ¼ 4 0 0 T
0 3 0
3 0 05 4
5. From Eq. (17.4.10) the transformation matrix D that takes CX to an identity matrix I is given by
pffiffiffiffi1 D¼M L
2 1 1 1 32 pffiffiffi pffiffiffi pffiffiffi 1 0 6 6 37 2 1 76 6 6 2 6 0 pffiffiffi 1 7 7 6 6 ¼ 6 pffiffiffi 0 pffiffiffi 76 3 3 74 6 6 4 1 1 1 5 0 0 pffiffiffi pffiffiffi pffiffiffi 3 6 2 3 2 1 1 1 p ffiffi ffi p ffiffi ffi p ffiffi ffi 6 6 6 2 37 7 6 pffiffiffi 7 6 2 1 7 6 ¼ 6 pffiffiffi 0 pffiffiffi 7 6 3 2 37 7 6 4 1 1 1 5 pffiffiffi pffiffiffi pffiffiffi 6 6 2 3
0
3
7 0 7 7 7 1 5 pffiffiffi 4
334
Random Vectors and Mean-Square Estimation
6. Since CY is a diagonal matrix, the random variables Y1, Y2, Y3 are independent, Gaussian, and zero mean. Hence the joint density is fY (y) ¼
1 (2p)
3=2
pffiffiffiffiffi e(1=2)½(y1 =1)þ(y2 =3)þ(y3 =4) 12 2
2
2
B 17.5 SIMULTANEOUS DIAGONALIZATION OF COVARIANCE MATRICES We are given two positive definite covariance matrices A and B with distinct eigenvalues. In pattern recognition problems it will be very useful to diagonalize both matrices by means of the same transformation matrix T. The transforming matrix T will take A to an identity matrix while at the same time diagonalizing B. We shall now analyze the procedure that will accomplish this. The two covariance matrices A and B with distinct eigenvalues are 0 1 0 1 a11 a12 a1n b11 b12 b1n B a21 a22 a2n C B b21 b22 b2n C B C B C A¼B . B¼B . (17:5:1) C . .. C . .. .. A .. @ .. @ .. . A . an1 an2 ann bn1 bn2 bnn We have seen in Section 16.2 how the modal matrix of eigenvectors F ¼ ff1,f2, . . . , fng of A will transform A to a diagonal matrix of its eigenvalues fl1,l2,l3, . . . , lng as shown: 0 1 l1 0 0 B 0 l2 0 C B C FT AF ¼ L ¼ B . (17:5:2) .. C .. @ .. . A . 0 0 ln From Eq. (17.4.9) we can write pffiffiffiffi pffiffiffiffi pffiffiffiffi1 pffiffiffiffi1 FT AF ¼ L In L or L FT AF L ¼ In pffiffiffiffi1 By defining U ¼ F L , we can write Eq. (17.5.3) as follows: pffiffiffiffi1 pffiffiffiffi1 L FT AF L ¼ UT AU ¼ In
(17:5:3)
(17:5:4)
This process is known as “whitening” the covariance matrix A. Since B is a covariance matrix, we can show that C ¼ U TBU is also a symmetric matrix as follows: pffiffiffiffi pffiffiffiffi1 T pffiffiffiffi1 pffiffiffiffi1 1 T T T T L F BF L ¼ L F B F L C ¼ ¼
pffiffiffiffi1 pffiffiffiffi1 L FT BF L ¼ C
(17:5:5)
The symmetric matrix C ¼ U TBU can therefore be diagonalized by its orthogonal modal matrix M, that is, M TCM ¼ M TU TBUM ¼ D ¼ diagfd1,d2, . . . , dng. The
This section is adapted, with permission, from Chapter 31 of the book Linear Systems Properties—a Quick Reference, by Venkatarama Krishnan, published by CRC Press, 1988.
17.5
Simultaneous Diagonalization of Covariance Matrices
335
transformation UM has diagonalized B, and now we will show that UM applied to A retains the whitening transformation. Using Eq. (17.5.4), we can write pffiffiffiffi1 pffiffiffiffi1 MT UT AUM ¼ MT L FT AF L M ¼ MT In M ¼ MT M (17:5:6)
¼ In
since M is an orthogonal matrix M TM ¼ In. Thus, the required transformation matrix T that takes A to an identity matrix and B to a diagonal matrix is given by pffiffiffiffi1 T ¼ F L M ¼ UM (17:5:7) With this transformation, we have TT AT ¼ In , TT BT ¼ D
(17:5:8)
For computational purposes the procedure described above can be simplified as follows. From Eq. (17.5.8) we can write AT ¼ TT
and
BT ¼ TT D
(17:5:9)
Hence BT ¼ ATD, and we have the generalized eigenequation A1 BT ¼ TD
ð17:5:10Þ
where the matrix T serves as the modal matrix for the product matrix A21B with D as its diagonal eigenvalue matrix. From Eq. (17.5.10) we can now formulate a procedure for finding the transformation matrix T that diagonalizes simultaneously the covariance matrices A and B. Summary of Procedure for Simultaneous Diagonalization 1. Calculate the n distinct eigenvalues fd1, d2, . . . , dng of the matrix R ¼ A21B from det (lI –R) ¼ 0. 2. Calculate the eigenvectors ti of R from the eigenequation (lI –R)ti ¼ 0. 3. Form the modal matrix N ¼ ft1, t2, . . . , tng. 4. Form the matrix N TAN ¼ W (normalization with respect to A). 5. Since with positive values, step 4 can be rewritten, as pffiffiffiffiffi W is diagonal pffiffiffiffiffi pffiffiffiffiffi ( W)1 NT AN( W)1 ¼ I and the desired transformation is T ¼ N( W)1 . Example 17.5.1
Two covariance matrices A and B are 0 1 0 2 1 1 4 A ¼ @ 1 3 1 A, B ¼ @2 1 1 4 2
given by 1 2 2 5 2A 2 6
There is no need to find the eigenvalues of A, and for comparison, the eigenvalues of B are 2 3 2:37 0 0 LB ¼ 4 0 3:52 0 5 0 0 9:11
336
Random Vectors and Mean-Square Estimation
1. The matrix R ¼ A – 1B is given by 2
2 R ¼ A1 B ¼ 4 0 0
3 0:176 0:235 1:588 0:118 5 0:059 1:412
2. The characteristic equation of R is (l 2 2)(l2 –3l þ 2.235) ¼ 0, and the eigenvalues are 2
3 0 0 1:621 0 5 0 1:379
2 D ¼ 40 0
3. The modal matrix N of eigenvectors of R is 2
3 0:525 0:188 0:82 0:481 5 0:23 0:856
1 N ¼ 40 0 4. The matrix W ¼ N TAN is given by 2
2 0 W ¼ 4 0 2:053 0 0
3 0 0 5 2:733
and
2 1:414 pffiffiffiffiffi W¼4 0 0
3 0 0 1:433 0 5 0 1:653
pffiffiffiffiffi 5. The desired transfromation T ¼ N W 21 is given by 2 0:707 0:366 pffiffiffiffiffi1 0:572 T¼N W ¼4 0 0 0:161
3 0:114 0:291 5 0:518
Comments 1. The matrix T is not orthogonal since 2
0:707
0
0
3
6 7 TT ¼ 4 0:366 0:572 0:616 5 = T1 0:114 0:291 0:518 2 3 1:414 0:707 0:707 6 7 ¼4 0 1:51 0:848 5 and jTj ¼ 0:243 0 0:161 0:518
17.6
Linear Estimation of Vector Variables
2. We can check the diagonalization process: 2 2 3 1 0 0 2 6 6 7 TT AT ¼ 4 0 1 0 5, TT BT ¼ 4 0 0 0
1
0 1:621
0 0
0
1:379 3
0 2
2:37 6 whereas LB ¼ 4 0 0
0 3:52 0
337
3 7 5
0 7 0 5 9:11
3. The diagonal elements in the transformation of B are the eigenvalues of R and not the eigenvalues LB of B.
B 17.6
LINEAR ESTIMATION OF VECTOR VARIABLES
We will take up more detailed estimation problems in the next chapter. We are interested in estimating the vector X given the equation Y ¼ H X þ N m1 mn n1 m1
(17:6:1)
where Y is an m 1 observation vector, H is an m n system vector, X is an n 1 vector to be estimated, and N is an m 1 noise vector with zero mean and covariance matrix, CN ¼ E[NN T] ¼ s2I. We need a linear estimator of the form ^ ¼ AY X
(17:6:2)
where A is an n m matrix to be determined. We have to choose a scalar criterion ˆ , which can be function J, which is some measure of the estimation error 1 ¼ Y 2 HX ˆ minimized with respect to the vector X. It is reasonable to choose a sum-squared error function given by T ^ ¼ Y HX ^ ^ J(X) Y HX (17:6:3) This is known as the linear least-squares estimation. Using Eq. (16.3.21), we can minimize Eq. (17.6.3) and write T @ ^ ¼ @ ^ ^ J(X) Y HX Y HX ^ ^ @X @X T @ T T T T ^ T T ^ ^ ^ ¼ Y Y X H Y Y HX þ X H HX ^ @X h i ^ ¼0 ¼ 0 ¼ 2HT Y þ 2HT HX ð17:6:4Þ or ^ ¼ HT H 1 HT Y X Thus, the desired n m matrix A in Eq. (17.6.2) is given by 1 A ¼ HT H HT
(17:6:5)
338
Random Vectors and Mean-Square Estimation
The quantity (H TH)21H T is called the pseudoinverse. We have essentially derived the ˆ deterministically without involving the noise covariance CN ¼ s2I. In this estimator X ˆ is not random. We ask the question as to the case the estimation error 1 ¼ Y 2 HX ˆ . If m . n, then we have an overdetermined meaning of this deterministic estimator X system where the number of observations are more than the unknowns and the n n ˆ is the one matrix H TH is invertible. In this case among the many possible solutions, X that gives a minimum mean-square solution. If m ¼ n, then H is a square matrix that may be invertible, in which case we have (H TH)21H T ¼ H 21H 2TH T ¼ H 21 and X ¼ H 21Y giving an exact solution. If m , n, then we have an underdetermined system where the number of observations is less than the number of unknowns and the ˆ can be given only in n n matrix H TH will be singular. The m-vector estimator X terms of the n 2 m unknown quantities of the vector X. ˆ random that estimates the The presence of the noise vector N makes the estimator X deterministic parameter vector X, and the criterion function J is J¼E
T ^ ^ Y HX Y HX
(17:6:6)
ˆ ¼ (H TH)21H TY is in the minimum mean-square error sense. and the estimator X ˆ ¼ (H TH)21H TY in Eq. (17.6.3), we can find the least-square By substituting X value as T Jmin ¼ Y HðHT HÞ1 HT Y Y HðHT HÞ1 HT Y ¼ YT I HðHT HÞ1 HT Y
(17:6:7)
since I HðHT HÞ1 HT I HðHT HÞ1 HT ¼ I HðHT HÞ1 HT or the matrix ðI HðHT HÞ1 HT Þ is idempotent. Example 17.6.1
An observation system, Y ¼ HX þ N, is given by 3 2 1 3 4 5 5 ¼ 42 1 10 2
Y
H
3 2 3 1 n1 x 4 5 1 þ 4 n2 5 x2 2 n3 X N
We have to estimate in the least-squares sense the 2-vector X. We also will find the noise sequence and the least squared error. From Eq. (17.6.4), we have 2 3 1 1 1 2 1 6 6 11 7 HT H ¼ 42 45 ¼ 1 4 2 11 6 1 2 2 3 2 3 21 11 2 1 2 6 T 1 T 6 5 1 2 1 5 7 5 5 7 7 7 H H H ¼6 ¼6 4 4 11 5 1 4 2 2 1 5 6 1 5 5 5 5
17.6
Linear Estimation of Vector Variables
ˆ is given by Hence the estimator X
^ ¼ HT H X
1
6 HT Y ¼ 11
11 6
1 1
2 4
2 3 3 1 4 5 2 5 ¼ 2 1 10
The error vector N is given by 2
3 2 3 2 3 3 3 0 ^ ¼ 4 5 5 4 8 5 ¼ 4 3 5 N ¼ Y HX 10 4 6 and the least-squared error Jmin is obtained from Eq. (17.6.7) as Jmin ¼ YT I H(HT H)1 HT Y ¼ 45
339
CHAPTER
18
Estimation Theory B 18.1
CRITERIA OF ESTIMATORS
Estimation is a process where we draw inferences about a set, random or otherwise, from knowledge of a suitably selected finite observation set. This inference is based on the minimization of some criterion function. The collection of elements in the entire set is called the population. The suitably selected observation set is called the sample. Any function of the elements of the sample is called an estimator or a statistic. Generally, estimators are random variables, but they can be constants under some circumstances. The criterion used for estimation in the previous chapter is the least-squares error. There are several other criteria for judging the proximity to the true value and the reasonableness of estimators. We shall enumerate some of these criteria assuming that u is a variable, random or otherwise, to be estimated by u^ from a set of finite observations: ^ of an estimator (u) ^ is defined by 1. Bias. The bias B(u) ^ ¼ E½u^ u B(u)
(18:1:1)
^ ¼ 0 and the pdf of the estimator is cenAn estimator is said to be unbiased if B(u) tered around u. Generally we would like to select an unbiased estimator. In some cases a biased estimator may be preferable to an unbiased one. 2. Variance. The variance of an estimator is defined by ^ ¼ s2^ ¼ E{u^ E½u} ^ 2 var(u) u
(18:1:2)
Generally we would choose an estimator with minimum variance. However, this may not be compatible with a minimum bias. 3. Mean-Square Error. The mean-square error is defined by ^ ¼ E½u^ u2 MSE (u)
(18:1:3)
^ ¼ E{u^ E½u ^ þ E½u ^ u}2 MSE(u) n o ^ 2 þ ðE½(u ^ u)2 þ (u^ E½u)(E½ ^ ^ u) ¼ E (u^ E½u) u n o ^ ¼ s2u^ þ E ðE½u^ uÞ2 þ ðu^ E½uÞðE½ u^ uÞ if u is a constant n o ^ ¼ s2u^ þ B2 if u^ is a constant (18:1:4) ¼ s2u^ þ B2 þ E ðu^ E½uÞB
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
340
18.1
Criteria of Estimators
341
If the estimator is unbiased, then the minimum mean square is the same as minimum variance. However, in the presence of a bias we will be able to obtain a mean-square error smaller than the variance. 4. Consistency. Let u^ n be an estimator obtained from n observations. Then u^ n is a consistent estimator if lim P½(u^ n u) . 1 ! 0
for every 1 . 0
n!1
(18:1:5)
or u^ n converges in probability to u as n ! 1: We would prefer to have a consistent estimator; otherwise we will have a bias for infinite samples that may not be desirable. 5. Maximum Likelihood. In Example 4.6.2 we estimated the population N of tigers in a wildlife sanctuary by maximizing the probability of occurrence of N using the maximum-likelihood criterion. We will expand on this concept further. The maximum-likelihood estimate of the parameter u from a probability density function f (x,u) of a random variable X is obtained by maximizing the likelihood function formed from n independent observations of the population fXi ¼ xi, i ¼ 1, . . . , ng. The likelihood function L(X,u) is the product of the density functions f(xi,u), i ¼ 1, . . . , n: L(X,u) ¼ f (x1 ,u) f (xn ,u)
(18:1:6)
Under certain regularity conditions the value of u^ that maximizes Eq. (18.1.6) is called the maximum-likelihood (ML) estimator. The monotonicity of the logarithm allows us to maximize the log likelihood function lnL(X,u). Thus the ML estimator is obtained from n @ @ @ X ln½L(X,u) ¼ ln ½ f (x1 ,u) . . . f (xn ,u) ¼ ln f (xi , u) @u @u @u i¼1
¼
n X i¼1
1 @f (xi ,u) f (xi ,u) @u
(18:1:7)
This method works well for large samples, but for small samples it may be biased. ^ and if it 6. Efficiency. The basic problem in estimation is to find the best estimator u, exists, it will be unique. However, in some problems finding the best estimator may not be simple. In such cases we are satisfied if we can find an estimator with the greatest lower bound. This bound is called the Rao –Cramer bound of an estimator, given by ^ ( var(u) E
1 @ ln f (X,u) @u
2 )
(18:1:8)
An unbiased estimate that satisfies the Rao – Cramer bound is called an efficient estimator.
342
Estimation Theory
B 18.2
ESTIMATION OF RANDOM VARIABLES
It is not possible for any estimator to satisfy all the criteria mentioned above. We will discuss unbiased minimum variance estimators under three broad categories: 1. Estimation of a random variable by a constant 2. Estimation of a random variable by a function of another random variable 3. Estimation of a constant by a random variable called parameter estimation We will discuss the first two in this section and the third one in the next section.
Estimation of a Random Variable by a Constant
This is the simplest of the estimation problems. A random variable X is to be estimated by a constant a such that the mean-square error J ¼ E[X 2 a]2 is minimized. J ¼ E½X a2 ¼ E½X 2 2Xa þ a2 dJ d ¼ ½E½X 2 2mX a þ a2 ¼ 2mX þ 2a ¼ 0 dx dx or a ¼ mX :
(18:2:1)
Thus, the constant that minimizes the mean-square error is the expected value E[X ]. The minimum mean-square error Jmin ¼ E[X – mX]2 ¼ s2X is the variance. Equation (18.2.1) can be interpreted in a different way. Since ð1 E½X a2 ¼ (x a)2 fX (x)dx (18:2:2) 1
the value of a that minimizes the mean-square error is ð1 xfX (x)dx ¼ mX
(18:2:3)
1
Estimation of a Random Variable Y by a Function of Another Random Variable g(X)
Here we address the problem of estimating a random variable Y by a function of another random variable g(X ) using the mean-square error criterion J ¼ E[Y – g(X )]2: ð1 ð1 J ¼ E½Y g(X)2 ¼ ½y g(x)2 fXY (x,y)dy dx (18:2:4) 1 1
Inserting fXY (x,y) ¼ fY j X ( y j x)fX (x) in Eq. (18.2.4), we obtain ð 1 ð 1 2 ½ y g(x) fY j X ( y j x)dy fX (x)dx J¼ 1
(18:2:5)
1
Minimizing Eq. (18.2.5) amounts to minimizing the inner integral ð1 ½ y g(x)2 fY j X ( y j x)dy 1
(18:2:6)
18.2
Estimation of Random Variables
343
From Eqs. (18.2.2) and (18.2.3), the value of g(x) that minimizes Eq. (18.2.6) is ð1 g(x) ¼ yfY j X (y j x)dy 1
or g(X) ¼ Y^ ¼ E½Y j X
(18:2:7)
Thus, the estimator Yˆ is the conditional expectation of Y given X. This estimator is unbiased because ^ ¼ E½Y j X ¼ E½Y E½Y It is also minimum variance, and the minimum mean-square error is the conditional variance: s2Y j X ¼ E{Y E½Y j X}2 In general, the conditional expectation E[Y j X ] is a complicated nonlinear function of X that may not be expressible in an explicit form. We confine ourselves to a linear function of the form E½Y j x ¼ g(x) ¼ Y^ ¼ a0 þ a1 x þ þ an xn ¼
n X
ai xi
(18:2:8)
i¼0
This form is known as the multiple linear regression, and the coefficients {ai} are called linear regression coefficients. It is linear because no powers of ai are involved. If E[Y j x] is of the form E½Y j x ¼ g(x) ¼ Y^ ¼ ax þ b
(18:2:9)
then it is called simple linear regression. The equation for the regression line is Y^ ¼ aX þ b
(18:2:10)
The estimator Y^ is unbiased because ^ ¼ E½aX þ b ¼ amX þ mY amX ¼ E½Y E½Y It is also minimum variance. Evaluation of Regression Coefficients
We will now evaluate the linear regression coefficients a and b for the case where Y is regressed on X, using the minimum mean-square error J ¼ E½Y aX b2
(18:2:11)
as the criterion function. We will first solve for b by differentiating J with respect to b: @J ¼ 2E½Y aX b ¼ 0 @b
344
Estimation Theory
Hence b ¼ mY amX
(18:2:12)
We can now solve for a by substituting Eq. (18.2.12) into Eq.(18.2.11) and differentiating J with respect to a: J ¼ E½Y mY a(X mX )2 @J ¼ 2E{½Y mY a(X mX )(X mX )} ¼ 0 @a ¼ E½(X mX )(Y mY ) a(X mX )2 ¼ 0 ¼ sXY as2X ¼ 0 Hence a¼
sXY s2X
and substituting
sXY ¼ rsX sY ,
a¼r
sY sX
(18:2:13)
The equations obtained by setting @J=@a ¼ 0 and @J=@b ¼ 0 are called normal equations. With these values of a and b the mean-square error can be found as follows: J ¼ E½Y mY a(X mX )2 ¼ s2Y 2asXY þ a2 s2X ¼ s2Y 2 ¼ s2Y
s2XY ¼ s2Y (1 r2 ) s2X
s2XY þ s2XY s2X (18:2:14)
The simple linear regression plot shown in Fig.18.2.1 indicates the slope a and the offset b. The line of regression divides the cluster of points into two halves such that the mean square between the points and the line of regression is minimum. In an analogous manner, we can also regress X on Y and find the corresponding regression coefficients a and b in the expression X^ ¼ aY þ b
FIGURE 18.2.1
(18:2:15)
18.2
Estimation of Random Variables
345
All the parameters such as the means, variances, and correlation coefficients will be the same. In much the same way as in Eqs. (18.2.12) and (18.2.13), the regression coefficients a and b are given by a¼
sXY s2Y
and
b ¼ mX amY
(18:2:16)
Substituting sXY ¼ rsX sY in Eq. (18.2.16), a can also be given as a¼r
sX sY
(18:2:17)
Multiplying the two slopes a and a, we obtain aa ¼ r
sY sX r ¼ r2 sX sY
(18:2:18)
Since the true means and variances are not known, Eq. (18.2.18) is a more accurate method for computing the correlation coefficient r in a real-life situation rather than using either Eq. (18.2.13) or (18.2.17). The sign of r can be obtained only from the physics of the problem. The regression coefficients for a multiple linear regression problem Y^ ¼ a0 þ a1 X1 þ þ an Xn can be obtained in principle by forming " J¼E Y
n X
(18:2:19)
#2 ai Xi
i¼0
and solving the (n þ 1) normal equations obtained from @J=@a0 ¼ 0, @J=@a1 ¼ 0, . . . , @J=@an ¼ 0: A simpler method will be discussed in the next section. Example 18.2.1 The relationship between random variables X and Y is shown in Table 18.2.1. The parameters of X and Y are given by mX ¼ 0:905, s2X ¼ 0:198, mY ¼ 1:939, s2Y ¼ 0:308, sXY ¼ 0:080:
TABLE 18.2.1 No.
X
Y
No.
X
Y
1 2 3 4 5 6 7 8 9 10
0.05 0.29 0.74 0.55 1.07 0.47 1.06 0.7 0.54 0.65
1.82 2.05 1.28 1.19 2.06 1.96 2.46 1.76 1.13 2.35
11 12 13 14 15 16 17 18 19 20
1.54 0.72 0.66 1.23 1.35 0.97 1.3 0.96 1.73 1.52
1.49 2.15 0.88 1.49 2.18 2.74 2.1 2.69 2.18 2.82
346
Estimation Theory
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The correlation coefficient rXY ¼ sXY =sX sY ¼ 0:08= (0:198 0:308) ¼ 0:324: We will find the regression coefficients a, b, a, b: a¼
sXY 0:08 ¼ 0:404, b ¼ my amX ¼ 1:939 0:404 0:905 ¼ 1:574 ¼ 2 0:198 sX
a¼
sXY 0:08 ¼ 0:26, b ¼ mX amY ¼ 0:905 0:26 1:939 ¼ 0:40 ¼ 0:308 s2Y
The regression lines y^ ¼ ax þ b and x^ ¼ ay þ b are shown in Figs. 18.2.2 and 18.2.3 respectively. The minimum mean-square error (MMSE) for Y on X regression ¼ 0.308 (1 2 0.3242) ¼ 0.276, and the MMSE for X on Y regression ¼ 0.194 (1 2 0.3242) ¼ 0.174. pffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The square root of the product aa ¼ 0:404 0:26 ¼ 0:324 ¼ rXY is the correlation coefficient obtained earlier. In general, the means and variances of random variables are not known a priori and we can only estimate them. We will revisit this example after discussing parameter estimation. Orthogonality Principle. We will motivate the orthogonality principle by taking the following expectations E½Y aX bb
(18:2:20a)
E½(Y aX b)aX
(18:2:20b)
with a ¼ sXY =s2X and b ¼ mY amX . Equation (18.2.20a) becomes E½Y aX bb ¼ ½mY amX ðmY amX )b ¼ 0: For Eq. (18.2.20b) we have E½(Y aX b)aX ¼ {E½XY aE½X 2 bmX }a ¼ {E½XY aE½X 2 (mY amX )mX }a ¼ {sXY as2X }a ¼ {sXY sXY }a ¼ 0 Combining both equations, we can write Y aX b aX þ b E ¼0 error 1 estimate Y^
FIGURE 18.2.2
18.2
Estimation of Random Variables
347
FIGURE 18.2.3
Equation (18.2.21) is the orthogonality principle, which states that under MMSE criterion the estimate Y^ is orthogonal to the error 1. In other words ^ ¼0 E½1Y
(18:2:21)
This is a powerful result that is used extensively in estimation problems. We can give a geometric interpretation to the orthogonality principle by means of Fig. 18.2.4. The two observations are b and a, represented by vectors x 0 and x 1 in a two-dimensional subspace. The unknown quantity y is not in the subspace. However, the estimate y^ , which is a linear combination of x 0, and x 1 is in the subspace. The error vector 1 is given by 1 ¼ y y^ is also shown in Fig. 18.2.4. From the geometry of the diagram, it can be seen that 1 is a minimum only when it is orthogonal to the subspace spanned by x 02x 1. Hence 1 is orthogonal to the estimate y^ . We will use the orthogonality principle to find the regression coefficients for a multiple linear regression problem of Eq. (18.2.19) expressed in matrix form Y^ ¼ a0 þ a1 X1 þ þ an Xn ¼ aT X T
(18:2:19)
where a is the (n þ 1) column vector [a0, a1, . . . , an] and X is the (n þ 1) random variable vector [1, X1, X2, . . . , Xn]T. Using the orthogonality principle that the error
FIGURE 18.2.4
348
Estimation Theory
1 ¼ Y aT X is orthogonal to the subspace spanned by the vector X, we have E½(Y aT X)XT ¼ 0
(18:2:22)
From Eq. (17.2.5) we can define E½XXT ¼ RX and E½XY ¼ RXY , and Eq. (18.2.22) can be rewritten as RX a ¼ E½XY
(18:2:23)
a ¼ R1 X E½XY
We can also find the minimum mean-square error using the orthogonality principle: J ¼ E½(Y aT X)(Y aT X) ¼ E½1(Y aT X)
(18:2:24)
Using orthogonality, we obtain Jmin ¼ E½1Y ¼ E½(Y aT X)Y ¼ E½(Y 2 aT E½XY
(18:2:25)
a result arrived at with comparative ease. Example 18.2.2 We will now solve the simple linear regression problem by using Eq. (18.2.23). We are to estimate Y in Y ¼ b þ aX þ 1. The estimator Y^ ¼ b þ aX. The vector a ¼ [b,a]T, and the vector X ¼ [1,X ]T. The correlation matrix RX is given by 1 mX 1 ½1 X ¼ RX ¼ E X mX m2 1 mY E½XY ¼ E Y ¼ X E(XY) Hence the regression coefficients from Eq. (18.2.23) are 1 mX 1 mY b m2 1 ¼ a¼ ¼ 2 sX mX a mX m2 E(XY) 1 m2 mY mX E(XY) ¼ 2 sX sXY 1 mY (m2 m2X ) mX (E½XY mX mY ) ¼ 2 sX sXY
mX 1
mY
E(XY)
or mY mX a b sXY ¼ a s2X which is the same result as obtained before but without differentiation. We can also find the minimum mean-square error using the orthogonality principle:
12 ¼ E½(Y aX b)(Y aX b) ¼ E½1(Y aX b)
18.2
Estimation of Random Variables
349
Using orthogonality, we obtain
12min ¼ E½1Y ¼ E½(Y aX b)Y ¼ E½Y 2 aE½XY mY ½mY amX ¼ E½Y 2 m2y a{E½XY mx mY } ¼ s2Y asXY ¼ s2Y (1 r2 ) a result arrived at with comparative ease. Example 18.2.3 A random variable Y ¼ aX 2 þ bX þ c þ 1 is to be estimated by a linear regression line Y^ ¼ aX 2 þ bX þ c. We want to find the coefficients a,b,c. The column vector a ¼ [a,b,c]T, and the column vector X ¼ [X 2,X,1]T. The correlation matrix RX is 82 3 9 2 3 m4 m3 m2 < X2 = (18:2:26) RX ¼ E 4 X 5½ X 2 X 1 ¼ 4 m3 m2 m 5 : ; m2 m 1 1 and 82 3 9 2 3 E½X 2 Y < X2 = E½XY ¼ E 4 X 5Y ¼ 4 E½XY 5 : ; E½Y 1
(18:2:27)
Hence the regression coefficients are 2 3 2 a m4 4 b 5 ¼ 4 m3 c m2
31 2 3 m2 E½X 2 Y m 5 4 E½XY 5 1 E½Y
m3 m2 m
(18:2:28)
Using Eq. (18.2.25), we can derive the minimum mean-square error as follows: 2
E½X 2 Y
3
6 7 12min ¼ E½Y 2 ½a,b,c4 E½XY 5 E½Y ¼ E½Y 2 {aE½X 2 Y þ bE½XY þ cE½Y}
(18:2:29)
Example 18.2.4 We will continue the previous example with observed data for random variables X and Y shown in Table 18.2.2. It is desired to estimate Y from X. The correlation matrix RX is given as 2
E½X 4 E½X 3 RX ¼ 4 E½X 3 E½X 2 E½X 2 E½X
3 2 4:53 E½X 2 5 4 ¼ 2:78 E½X 1:79 1
2:78 1:79 1:23
3 1:79 1:23 5 1
In addition, the cross-moments of X with Y are E[X 2Y ] ¼ 3.79, E[X Y ] ¼ 2.67, with E[Y ] ¼ 2.21 and E[Y 2] ¼ 4.92.
350
Estimation Theory
TABLE 18.2.2 No.
X
Y
No.
X
Y
1 2 3 4 5 6 7 8 9 10 11
0.91 0.22 0.23 1.92 1.87 0.55 0.94 1.4 1.06 1.38 1.91
2.6 2.21 2.2 2.03 2.07 2.38 2.48 2.37 2.44 2.34 1.9
12 13 14 15 16 17 18 19 20 21
1.47 1.37 0.63 1.39 0.79 1.53 1.51 1.35 1.86 1.64
2.25 2.28 2.26 2.23 2.27 2.11 2.1 2.17 1.77 1.95
FIGURE 18.2.5
The regression coefficients are obtained from Eq. (18.2.28) as follows: 3 2 3 2 3 2 31 2 3:79 0:46 a 4:53 2:78 1:79 4 b 5 ¼ 4 2:78 1:79 1:23 5 4 2:67 5 ¼ 4 0:79 5 2:21 2:06 c 1:79 1:23 1 The line of regression is y ¼ 0:46x2 þ 0:79x þ 2:06. The original data y and the line of regression are shown in Fig. 18.2.5. The minimum mean-square error is obtained from Eq. (18.2.29) and is given by 12min ¼ E½Y 2 aE½X 2 Y þ bE½XY þ cE½Y ¼ 0:0109
B 18.3 ESTIMATION OF PARAMETERS (POINT ESTIMATION) A population is represented by the random variable X characterized by a parameter u, which is an unknown constant, and whose distribution function FX(x,u) is of a known form. We estimate this parameter by observing n realizations {x1, . . . , xn} of the
18.3
Estimation of Parameters (Point Estimation)
351
random variable X. Associated with each of these realizations is a set of random variables {X1, . . . , Xn} having the same characteristics as the population. We form a function ^ If X is the n-vector equal to [X1, . . . , Xn]T, then u^ = g(X1, . . . , Xn) for the estimator u. g(X) is known as the point estimator for u. As mentioned earlier, any function g(X) is ^ we cannot conclude that also called a statistic for u. Having determined the estimator u, it is exactly equal to the parameter u. However, we may be able to conclude that u is in the proximity of u^ within the interval u1 ¼ g1(X) and u2 ¼ g2(X). The interval estimator for u is the random interval, fQ1, Q2g, such that P{Q1 , u , Q2 } ¼ g
(18:3:1)
where g is called the confidence coefficient and the interval (u1,u2) is the g –confidence interval. The coefficient a ¼ (1 2 g) is called the significance level. We will discuss interval estimators in the next section.
Estimation of Mean
The point estimator m^ X , called the sample mean for the mean mX of the random variable X, can be given as the weighted sum of the independent observation random variables fX1, . . . , Xng. Thus m^ X ¼
n X
a i Xi ¼ aT X
(18:3:2)
i¼1
where the weight vector a has to be determined according to the criteria of unbiasedness and minimum variance. The unbiasedness criterion applied to Eq. (18.3.2) yields " # n n n X X X E½m^ X ¼ E ai Xi ¼ ai E½Xi ¼ mX ai ¼ m X (18:3:3) i¼1
i¼1
i¼1
Hence we have the first condition: n X
ai ¼ 1
(18:3:4)
i¼1
The variance of the estimator m^ X of Eq. (18.3.2) under the assumption that the Xi values are independent is given by var(m^ X ) ¼ s2m^ X ¼
n X
a2i u2X
(18:3:5)
i¼1
P The problem is to minimize var(m^ X ) subject to the constraint ni¼1 ai ¼ 1: We make use of Lagrange multipliers to find a solution. We treat this problem as an unconstrained minimization by adjoining the constraint equation as shown below: ! n n X X 2 2 J¼ a i sX þ l ai 1 (18:3:6) i¼1
i¼1
P where l is the Lagrange multiplier that has to be determined. Since l ni¼1 ai 1 ¼ 0, we have not changed the minimization criterion. We treat Eq. (18.3.6) as unconstrained
352
Estimation Theory
and differentiate with respect to ai: " !# n n X @J @ X 2 2 ¼ a s þl ai 1 @ai @ai i¼i i X i¼1 ¼ 2ai s2X þ l ¼ 0
and
ai ¼
l , 2s2X
i ¼ 1, . . . , n
(18:3:7)
We have determined faig in terms of the unknown Lagrange multiplier l. Substituting for ai from Eq. (18.3.7) into the constraint Eq. (18.3.4), we obtain n X l ¼1 2 2s X i¼1
or l ¼
2s2X n
(18:3:8)
Substituting Eq. (18.3.8) back into Eq. (18.3.7), we obtain the weighting coefficients ai as 1 ai ¼ , i ¼ 1, . . . , n n
(18:3:9)
and the minimum variance unbiased estimator for mX is given by m^ X ¼
n 1X Xi n i¼1
(18:3:10)
We can compute the variance of m^X from Eq. (18.3.5): var(m^ X ) ¼ s2m^ X ¼
n X s2 i¼1
X n2
¼
s2X n
(18:3:11)
In Eq. (18.3.11), as n ! 1, variance of m^ X tends to 0. We have already shown in Eq. (14.1.2) that if the variance is zero, then m^ X ! mX with probability 1. Hence we can conclude that m^ X is a consistent estimator since it tends to mX as n ! 1. The density of m^ X , which is centered at mX , is shown in Fig. 18.3.1 for different values of n.
FIGURE 18.3.1
18.3
Estimation of Parameters (Point Estimation)
353
Minimum Mean-Square Estimator for the Mean
Following the lines of Eq. (18.3.3), we will find a minimum mean-square estimator (MMSE) m X such that mX ¼
n X
a i Xi
and
E½mX ¼
n X
i¼1
ai E½Xi ¼ kmX
(18:3:12)
i¼1
and the corresponding bias BmX in the estimator mP X is BmX ¼ (k 1)mX . We have to find n 2 2 2 the coefficients fa g such that the variance s ¼ i i¼1 ai sX is minimized subject to the mX Pn constraint i¼1 ai ¼ k. Using Lagrange multipliers as in the previous case, the coefficients are found to be k ai ¼ , n
i ¼ 1, . . . , n
(18:3:13)
and the corresponding minimum variance is equal to s2mX ¼
k2 s2X n
(18:3:14a)
and the biased estimator in terms of the unbiased estimator is given by k mX ¼ m^ X n
(18:3:14b)
We will find k that minimizes the mean-square error criterion given by MSE ¼ 12 ¼ E½mX mX 2 ¼ E½(mX E½mX ) þ (E½mX mX )2 ¼ s2mX þ B2mX ,
E½(mX E½mX ) ¼ 0
(18:3:15)
Substituting Eqs. (18.3.3) and (18.3.4) in Eq. (18.3.15), we have
12 ¼ s2mX þ B2mX ¼
k2 s2X þ (k 1)2 m2X n
(18:3:16)
Differentiating Eq. (18.3.16) with respect to k, we obtain @ 2 s2 ½1 ¼ 2k X þ 2(k 1)m2X ¼ 0 @k n or kmin ¼
m2X s2X þ m2X n
and
mX ¼
m2X s2X þ m2X n
m^ X
(18:3:17)
The variance of the estimator mX is obtained by substituting for k in Eq. (18.3.14a) and 2 s2mX ¼ kmin
s2X m4X s2X ¼ 2 n n s2X þ m2X n
(18:3:18)
354
Estimation Theory
which is less than (m^ X ) but biased with bias equal to
B mX
s2X mX ¼ (kmin 1)mX ¼ 2n sX þ m2X n
(18:3:19)
However, mX is consistent and asymptotically unbiased as n ! 1. This may not be a useful estimator because it is dependent on s2X and mX , which are both unknown quantities. At the very least, the ratio sX =mX must be known. This example illustrates that biased estimators in some cases may be better estimators than unbiased ones.
Estimation of Variance
Analogous to formulation of the sample mean, we can express the sample variance to be of the form s2X ¼
n 1X (Xi mX )2 n i¼1
(18:3:20)
and indeed s2X will be an unbiased minimum variance estimator; but mX is unknown, and hence s2X may not be a useful estimator. However, if we substitute the sample mean m^ X instead of mX in Eq. (18.3.20), then we can write the new estimator s~ 2X as follows: s~ 2X ¼
n 1X (Xi m^ X )2 n i¼1
(18:3:21)
To determine whether s2X is unbiased, we take expectations on both sides of Eq. (18.3.21) and write ( " #) n n n n X 1X 2 X 1X 2 2 X Xi Xj þ 2 Xj Xk E(s~ X ) ¼ E n i¼1 i n j¼1 n j¼1 k¼1 8 2 39 > > = n n n n X n <1 X X X X 1 1 6 2 2 2 2 7 2 ¼E (18:3:22) X i Xj þ 2 Xj þ 2 Xj Xk 5 4 Xi Xi > > n n j¼1 n j¼1 n j¼1 k¼1 ; :n i¼1 j=i
n 1X
j=k
2 n 2(n 1) 2 n(n 1) 2 2 2 ¼ mX þ 1 þ 2 (sX þ mX ) mX n i¼1 n n n n2 n 1X n1 2 n 1 2 2(n 1) 2 (n 1) 2 n1 2 mX þ mX ¼ sX ¼ sX þ mX n i¼1 n n n n n As we can see from Eq. (18.3.22), the estimator s~ 2X is biased. To obtain an unbiased estimator, we have to divide Eq. (18.3.21) by (n 2 1) instead of by n and write the unbiased minimum variance estimator s^ 2X , also called the sample variance, as s^ 2X ¼
n 1 X (Xi m^ X )2 n 1 i¼1
(18:3:23)
In forming an unbiased estimator for the variance, the degrees of freedom in the estimator determines the divisor. In Eq. (18.3.20) there are n degrees of freedom since there are n
18.3
Estimation of Parameters (Point Estimation)
355
independent values of Xi, and hence the divisor P is n. However, in Eq. (18.3.23) there are only (n 2 1) degrees of freedom since m^ X ¼ ni¼1 (Xi =n), which takes away one degree of freedom. Thus, the divisor is (n 2 1). The square root of the sample variance s^ X is called the standard error. We will now compute the variance of the sample variance "
var(s^ 2X )
#2 n 1 X 2 ¼E (Xi m^ X ) (s2X )2 n 1 i¼1 ( ) n n X X 1 2 2 ¼E (Xi m^ X ) (Xj m^ X ) s4X (n 1)2 i¼1 j¼1 8 2 39 n n X n < 1 = X X 4 2 25 4 s4X ¼E (X m ^ ) þ (X m ^ ) (X m ^ ) i i j X X X :(n 1)2 i¼1 ; i¼1 j¼1 i=j
n n(n 1) 4 n s4 v þ s s4X ¼ v þ X 2 4 2 X 2 4 n1 (n 1) (n 1) (n 1)
(18:3:24)
where v4 ¼ E(Xi mX )4 from Eq. (10.4.2). We can also confirm that s^ 2X is a consistent estimator since var(s^ 2X ) tends to 0 as n tends to 1. In fact, all three estimators, s2X , s~ 2X , and s^ 2X , are consistent while only s2X and s^ 2X are unbiased. However, for large n all the estimators are asymptotically unbiased.
Estimation of Covariance
The unbiased estimator for the covariance sXY between two random variables X and Y can be derived with the following assumptions. Corresponding to each of the random variables, we have two sets of independent observations fX1, . . . , Xng and fY1, . . . , Yng with E[Xi] ¼ mX, E[Yi] ¼ mY, E[XiYi] ¼ sXY þ mX mY, and E[XiYj] ¼ mX mY for i = j. The estimator s^ XY for the covariance can be expressed as s^ XY ¼
n 1X (Xi m^ X )(Yi m^ Y ) K i¼1
(18:3:25)
where the divisor K is found from the following steps to satisfy the unbiasedness condition E(s^ XY ) ¼ sXY : " # n X X E (Xi m^ X )(Yi m^ Y ) ¼ E½Xi Yi Xi m^ Y Yi m^ X þ m^ X m^ Y i¼1
i
X 1X 1X 1 XX Y j Yi Xj þ 2 Xi Yj ¼ E Xi Yi Xi n n n i " X Xi Yi X Xi Yj Xi Yi X Yi Xj 1 X þ 2 Xi Yi E Xi Yi ¼ n n n n n i j=i j=1 # 1 XX þ 2 Xi Yj n j=i i
356
Estimation Theory
X
1 2(n 1) n(n 1) mX mY þ ¼ (sXY þ mX mY ) 1 mX mY n n n2 i Xn 1 n1 2(n 1) n1 sXY þ mX mY mX mY þ mX mY ¼ n n n n i (18:3:26)
¼ (n 1)sXY
From Eq. (18.3.26) K is found to be (n 2 1), and the unbiased minimum variance estimator for the covariance is s^ XY ¼
n 1 X (Xi m^ X )(Yi m^ Y ) n 1 i¼1
(18:3:27)
a result very similar to that of the estimator s^ 2X of the variance. We will now compute the variance of the sample covariance "
#2 n 1 X (Xi m^ X )(Yi m^ Y ) (sXY )2 var(s^ XY ) ¼ E n 1 i¼1 ( ) n n X
X
1 ¼E (Xi m^ X )(Yi m^ Y ) (Xj m^ X )(Yj m^ Y ) s2XY (n 1)2 i¼1 j¼1 8 2 n 39 P 2 > > > > ½(X m^ X )(Yi m^ Y ) > = < 1 6 i¼1 i 7> 6 7 s2XY ¼E n n 6 7 P P 24 > 5> (n 1) > > þ ½(X m ^ )(Y m ^ )½(X m ^ )(Y m ^ ) i i j j > > X Y X Y ; : i¼1 j¼1 j=i
n n(n 1) 2 n s2 v XY þ s s2XY ¼ vXY þ XY 2 2 2 XY 2 2 n1 (n 1) (n 1) (n 1)
(18:3:28)
2 where we have defined vXY as the second cross-central 2 ¼ E ½(Xi mX )(Yi mY ) moment between X and Y. Since var(s^ XY ) tends to 0 as n tends to 1, we conclude that (s^ XY ) is also a consistent unbiased minimum variance estimator. In Eqs. (18.2.13) and (18.2.17) the means and variances were explicitly stated. In general, these quantities can only be estimated. We will revisit the linear regression problem later. Example 18.3.1 We will find the estimated means and variances of the random variables X and Y from the data given in Example 18.2.1. From Eq. (18.3.10) the unbiased, minimum variance estimators for the mean of X and Y are m^ X ¼
1 18:1 ½0:05 þ 0:29 þ þ 1:52 ¼ ¼ 0:905 20 20
m^ Y ¼
1 38:78 ½1:82 þ 2:05 þ þ 2:82 ¼ ¼ 1:939 20 20
18.3
Estimation of Parameters (Point Estimation)
357
Similarly, from Eq. (18.3.9) the estimated variances of X and Y are 1 ½(0:05 0:905)2 þ (0:29 0:905)2 þ þ (1:52 0:905)2 19 3:767 ¼ 0:198 ¼ 19 1 s^ 2Y ¼ ½(1:82 1:939)2 þ (2:05 1:939)2 þ þ (2:82 1:939)2 19 5:857 ¼ 0:308 ¼ 19
s^ 2X ¼
The estimated covariance between X and Y is 1 ½(0:05 0:905)(1:82 1:939) þ þ (1:52 0:905)(2:82 1:939) 19 1:521 ¼ ¼ 0:080 19
s^ X Y ¼
The estimated correlation coefficient is r^ XY ¼
s^ XY 0:08 ¼ 0:324 ¼ s^ X s^ Y 0:247
These parameters are the same numbers that we used in Example 18.2.1. However, the regression coefficients a and b will be estimates and will be governed by a probability distribution. Example 18.3.2 We will now find the estimated means and variances of the random variables X and Y from the data given in Example 18.2.3. From Eq. (18.3.10) the unbiased, minimum variance estimators for the mean of X and Y are m^ X ¼
1 25:93 ½0:91 þ 0:22 þ þ 1:64 ¼ ¼ 1:235 21 21
m^ Y ¼
1 46:41 ½2:6 þ 2:21 þ þ 1:95 ¼ ¼ 2:21 21 21
Similarly, from Eq. (18.3.9) the estimated variances of X and Y are 1 ½(0:91 1:235)2 þ (0:22 1:235)2 þ þ (1:64 1:235)2 20 5:50 ¼ 0:275 ¼ 20
s^ 2X ¼
1 ½(2:6 2:21)2 þ (2:21 2:21)2 þ þ (1:95 2:21)2 20 0:795 ¼ 0:0398 ¼ 20
s^ 2Y ¼
358
Estimation Theory
We will now find estimated values for the higher-order moments of X. They are given by ^ 4 ¼ ^ 4X ¼ E½X m
1 95:1 ½0:914 þ 0:224 þ þ 1:644 ¼ ¼ 4:53 21 21
^ 3 ¼ ^ 3X ¼ E½X m
1 58:43 ½0:913 þ 0:223 þ þ 1:643 ¼ ¼ 2:78 21 21
^ 2 ¼ ^ 2X ¼ E½X m
1 37:52 ½0:912 þ 0:222 þ þ 1:642 ¼ ¼ 1:79 21 21
The estimated second moment of Y is given by ^ 2 ¼ ^ 2X ¼ E½Y m
1 103:36 ½2:62 þ 2:212 þ þ 1:952 ¼ ¼ 4:92 21 21
The estimated cross-moments of X with Y are 1 56:1 ^ E½XY ¼ ½0:91 2:6 þ 0:22 2:21 þ þ 1:64 1:95 ¼ ¼ 2:67 21 21 ^ 2 Y ¼ 1 ½0:912 2:6 þ 0:222 2:21 þ þ 1:642 1:95 ¼ 79:6 ¼ 3:79 E½X 21 21 The estimated correlation matrix RX is, 2
^ 4 E½X ^ X ¼ 4 E½X ^ 3 R ^ 2 E½X
3 2 ^ 3 E½X ^ 2 4:53 E½X 5 ¼ 4 2:78 ^E½X 2 E½X ^ ^ 1:79 E½X 1
2:78 1:79 1:23
3 1:79 1:23 5 1
These values are the same values given in Example 18.2.3. Again, the regression coefficients a, b, and c will be only estimates. Estimated Regression Coefficients from Data
As stated in previous examples, the evaluated regression coefficients will be estimates of the true values since the means and variances are only estimates. We will now discuss the methodology of finding the estimated regression coefficients and determining their variances from the given data. The linear regression problem for each yi can be stated as yi ¼ a1 xi1 þ þ am xim þ wi ¼ xTi a þ wi ,
i ¼ 1, . . . , n
with n . m. Or more succinctly y ¼ XT a þ w
(18:3:29)
where the n-vector y is the realization of the random variable Y with E(y) ¼ X Ta where a is an unknown m-vector parameter to be estimated, and xi is the observation m-vector given by xi ¼ [xi1, . . . , xim]T. In linear regression, the coefficient a1 represents the offset and hence xi1¼1 for all i. The n-vector w is a realization of the measurement noise random variable W. The elements wi of w are assumed to be uncorrelated with zero mean and variance E[w2i ] ¼ s2W for all i. X is an m n (n . m) matrix of
18.3
Estimation of Parameters (Point Estimation)
359
observations given by x2 ½x 2 1 x11 x21 6x 6 12 x22 6 . .. 6 . . . X¼ 6 6 6 x1j x2j 6 6 . .. 6 . 4 . .
xi xi1 xi2 .. . xij .. .
xn 3 2 1 xn1 7 6 xn2 7 6 x12 6 .. 7 7 6 . . 7 6 .. 7¼6 6 xnj 7 7 6 x1j 7 .. 7 6 6 . . 5 4 ..
x1m x2m xim xmn
1 x22 .. . x2j .. .
3 1 1 xi2 xn2 7 7 .. 7 .. 7 . 7 . 7 (18:3:30) xij xnj 7 7 .. 7 .. 7 . 5 .
x1m x2m xim xnm
Given this information, we have to estimate the regression coefficient vector a using the least-squares error. Thus, the criterion function J(a) is given by J(a) ¼ (y XTa )T (y XTa ) ¼ yT y aT Xy yT XT a þ aT XXT a
(18:3:31)
From Eq. (16.3.4) the derivative with respect to the vector a of Eq. (18.3.31) is @ @ J(a) ¼ ½yT y aT Xy yT XT a þ aT XXT a @a @a ¼ 2Xy þ 2XXTa ¼ 0
(18:3:32)
From Eq. (18.3.32) the estimated regression coefficients a^ are given by a^ ¼ (XXT )1 Xy T
where XX expanded in terms of the P 2 n xi2 n 6P P 6 xi2 x2i2 6 n n 6 6 . .. 6 . 6 . T P. XX ¼ 6 P 6 xij xij xi2 6 n n 6 6 . .. 6 .. 4P P . xim xim xi2 n
n
(18:3:33)
observation points fxijg is given by P P 3 xij xim n n 7 P P xi2 xij xi2 xim 7 7 n n 7 7 .. .. 7 7 . . P 2 P 7 xij xij xim 7 7 n n 7 7 .. .. 7 . . P P 2 5 xim xij xim n
(18:3:34)
n
The equation for the regression line is given by y^ ¼ XT a^
(18:3:35)
The estimator a^ is an unbiased estimator of a since E(^a) ¼ (XXT )1 XE(y) ¼ (XXT )1 XXT a ¼ a
Covariance of the Estimated Regression Coefficients a^
Before finding the covariance of a, we will express E½^a a in a more convenient form given by E½^a a ¼ E½(XXT )1 Xy a ¼ E½(XXT )1 X(XT a þ w) a ¼ E½(XXT )1 Xw
(18:3:36)
360
Estimation Theory
The covariance matrix Ca^ of the estimated regression coefficients can now be given by Ca^ ¼ cov(^a) ¼ cov½(^a a)(^a a)T ¼ cov{(XXT )1 XwwT XT (XXT )T } ¼ (XXT )1 X cov(wwT )XT (XXT )T ¼ (XXT )1 Xs2w IXT (XXT )T ¼ (XXT )1 s2w
(18:3:37)
where cov(wwT ) ¼ s2w Im . The variances of the regression coefficients are given by the diagonal terms of the covariance matrix Ca^ . Since we do not know the variance s2w of the measurement noise, we use its estimate s^ 2w , given by s^ 2w ¼
(y y^ )T (y y^ ) (y XT a^ )(y XT a^ ) ¼ nm nm
(18:3:38)
In this equation, the divisor (n 2 m) reflects the reduction in the number of degrees of freedom n by the number of regression coefficients m. This yields an unbiased estimator for s2w.
Variance of the Estimated Regression line y^ i
We will now find the variance of the estimated mean y^ i , given by y^ i ¼ a^ 1 xi1 þ þ a^ m xim ¼ a^ T xi ,
i ¼ 1, . . . , n
(18:3:39)
where y^ i is an unbiased estimator of yi since E½^yi ¼ y. The variance of y^ i can be given by E½ y^ i yi 2 ¼ E½(^yi yi )T (^yi yi ) ¼ xTi E ½(^a a)(^a a)T xi ¼ xTi cov(^a)xi
(18:3:40)
Substituting for cov(^a) from Eq. (18.3.37), we have var( y^ i ) ¼ s2y^ i ¼ xTi (XXT )1 xi s^ 2w , i ¼ 1, . . . , n
(18:3:41)
and since we do not know the variance, we substitute the estimated variance s^ 2w and obtain the estimated variance of y^ i as s^ 2y^ i ¼ xTi (XXT )1 xi s^ 2w ,
i ¼ 1, . . . , n
(18:3:42)
which is a function of i, and is chi-square-distributed with (n 2 m) degrees of freedom.
Simple Linear Regression
The governing equation for a simple linear regression is yi ¼ a1 þ a2 xi þ wi ,
i ¼ 1, . . . , n
(18:3:43)
with the usual definitions. From the data, we will derive expressions for the estimated regression coefficients for a1 and a2. The normal equations can be obtained from Eqs. (18.3.32) and (18.3.34) as follows: X 3 2 2 X 3 xi yi n 6X 6X 7 a^ 1 7 n n X (18:3:44) ¼4 4 5 5 2 ^ a 2 xi xi xi yi n
n
n
18.3
Estimation of Parameters (Point Estimation)
361
The determinant of the rectangular matrix of Eq. (18.3.44) is X
!2 xi X X n 2 X ¼n xi xi x2i n n n X X ¼n x2i m2 m^ 2x ¼ n (xi m^ x )2
n X xi n
n
(18:3:45)
n
P where m^ x ¼ (1=n) n xi . Solving Eq. (18.3.44) and substituting in Eq. (18.3.45), the estimated coefficients a^ 1 and a^ 2 are
a^ 1 a^ 2
2 X
x2i 1 6 nX ¼ X 4 (xi m^ x )2 n xi n
2X
n
X
xi
n
n
X
32 X 76 X n 54
yi
xi yi
3 7 5
n
X
X
3 yi xi xi yi 1 6 n Xn 7 n n X X ¼ X 4 5 n (xi m^ x )2 n xi yi xi yi x2i
n
n
n
(18:3:46)
n
In Eq. (18.3.46) n
X
xi yi
X
n
xi
X
n
yi ¼ n
X
n
xi yi n2 m^ x m^ y ¼ n
n
X
(xi m^ x )(yi m^ y )
n
Hence X n
a^ 2 ¼
(xi m^ x )(yi m^ y ) X (xi m^ x )2
(18:3:47)
n
Also, in Eq. (18.3.46) X
x2i
n
X n
yi
X n
xi
X
xi yi ¼ nm^ y
X
n
n
x2i
X n
" xi
X
# (xi m^ x )(yi m^ y ) þ nm^ x m^ y
n
"
# X
X x2i m^ 2X nm^ x (xi m^ x )(yi m^ y ) ¼ nm^ y n
n
Hence X a^ 1 ¼ m^ y
n
(xi m^ x )(yi m^ y ) X m^ x ¼ m^ y a^ 2 m^ x (x2i m^ 2X )
(18:3:48)
n
The estimated regression line equation is y^ i ¼ a^ 1 þ a^ 2 xi , i ¼ 1, . . . , n
(18:3:49)
362
Estimation Theory
We can now compute the variances of a^ 1 and a^ 2 from Eqs. (18.3.33) X 3 2 X 2 xi xi 1 a^ 1 6 7 2 n n var ¼ X 4 X 5s^ w a^ 2 (xi m^ x )2 n xi n n
(18:3:50)
n
where we have substituted the estimated variance s^ 2w from Eq. (18.3.38) for the true variance s2w . Simplifying Eq. (18.3.50), we obtain the variances of a^ 1 , and a^ 2 as var(^a1 ) ¼ s^ 2a^ 1 ¼ X n
s^ 2w (xi m^ x )2
2X
6 var(^a2 ) ¼ s^ 2a^ 2 ¼ s^ 2w 4
n
2 3 3 (xi m^ x )2 þ n(m^ 2x ) 2 m^ x 61 7 7 X 5 ¼ s^ 2w 4 þ X 2 25 n (xi m^ x ) (xi m^ x ) n n
(18:3:51)
n
Finally, we can compute the estimated variance of the estimated regression line y^ i . From Eq. (18.3.42), we have X 3 2 X 2 xj xj
6 n 1 7 1 2 n 2 X X s^ y^ i ¼ 1 xi 4 s^ , i ¼ 1, . . . , n (18:3:52) 5 2 xi w (xj m^ x ) n xj n n
n
Equation (18.3.52) can be simplified as follows P 3 2 P 2 xj xj X X 1 n 5 x2j 2xi xj þ nx2i ¼ ½ 1 xi 4 nP xj n xi n n n
¼
X
2 xj m^ x þn(xi m^ x )2
(18:3:53)
n
where we have subtracted and added m^ 2x . Substituting Eq. (18.3.53) in Eq. (18.3.52), we obtain " # X 1 2 2 2 s^ y^ i ¼ P (xj m^ x ) þ n(xi m^ x ) s^ 2w n n (xj m^ x )2 n 1 (xi m^ x )2 þP ¼ (18:3:54) s^ 2 , i ¼ 1, . . . , n n ^ x )2 w n (xj m Example 18.3.3 From the data in Example 18.2.1 we will determine the estimated regression coefficients and their variances using the techniques developed above. The linear equation used to solve for the regression coefficients is y i ¼ a1 þ a2 x i þ w i ,
i ¼ 1, . . . , 20
In Eq. (18.3.29) the 20-vector y is given by 1:82, 2:05, 1:28, 1:19, 2:06, 1:96, 2:46, 1:76, 1:13, 2:35, T y¼ 1:49, 2:15, 0:88, 1:49, 2:18, 2:74, 2:10, 2:69, 2:18, 2:82
18.3
Estimation of Parameters (Point Estimation)
363
and the 20-vector x is given by, 0:05, 0:29, 0:74, 0:55, 1:07, 0:47, 1:06, 0:7, 0:54, 0:65, T x¼ 1:54, 0:72, 0:66, 1:23, 1:35, 0:97, 1:3, 0:96, 1:73, 1:52 a is a 2-vector of regression coefficients given by a ¼ ½a1 ,a2 T and w is an unknown measurement 20-vector noise whose variance can only be estimated. We can now form the 2 20 matrix X, where the first row is always 1 since a1 multiplies 1 for all i. Hence the matrix X is 82 39 1 1 1 1 1 1 1 1 1 1 > > > = <6 0:05 0:29 0:74 0:55 1:07 0:47 1:06 0:7 0:54 0:65 7> 7 X¼ 6 4 1 > 1 1 1 1 1 1 1 1 1 5> > > ; : 1:54 0:72 0:66 1:23 1:35 0:97 1:3 0:96 1:73 1:52 The quantity XXT and (XXT )1 are given by 20 18:1 0:267 (XXT )1 ¼ XXT ¼ 18:1 20:147 0:24
0:24 0:265
The estimated regression coefficients a^ are obtained from Eq. (18.3.33): a^ 1:574 0:267 0:24 38:78 aˆ ¼ 1 ¼ (XXT )1 (Xy) ¼ ¼ 0:404 0:24 0:265 36:617 a^ 2 with Xy ¼
38:78 36:617
where a^ 1 and a^ 2 exactly match the quantities b and a in Example 18.2.1. The equation of the regression line y^ i ¼ a^ 1 þ a^ 2 xi ¼ 1:574 þ 0:404xi ,
i ¼ 1, . . . , 20
has already been shown in Fig. 18.2.2. We will now compute the estimated variance of the noise term from Eq. (18.3.38): s^ 2W ¼
(y XT aˆ )T (y XT aˆ ) 4:74 ¼ ¼ 0:263 nm 20 2
This compares within the limits of data analysis with MMSE ¼ 0.276, obtained in Example 18.2.1. From Eq. (18.3.37), the covariance matrix of a^ is 0:0704 0:0633 0:267 0:24 Caˆ ¼ cov(ˆa) ¼ (XXT )1 s^ 2W ¼ 0:263 ¼ 0:0633 0:0699 0:24 0:266 Hence var(^a1 ) ¼ 0:0704 and var(^a2 ) ¼ 0:0699. We can now obtain the variance ofPy^ i using Eq. (18.3.54), where n ¼ 20, the average value m^ x ¼ 0:905, and the summation n (xj m^ x )2 ¼ 3:7665. Substituting these values, we obtain 2 3 2 (xi m^ x ) 7 1 (xi 0:905)2 2 26 1 þ s^ y^ i ¼ s^ w 4 þ P , i ¼ 1, . . . , 20 5 ¼ 0:263 20 20 3:7665 (xj m^ x )2 20
364
Estimation Theory
B 18.4 INTERVAL ESTIMATION (CONFIDENCE INTERVALS) In the previous section we discussed point estimators u^ for a parameter u. However, a point estimator may not be meaningful without knowledge of the probability of how far away it is from the true value. Hence, we would also like to know an interval about the estimator u^ and the probability that the true value u lies in that interval. Such an interval for u is the random interval, fQ1, Q2g, such that P{Q1 , u , Q2 } ¼ g
(18:4:1)
where g is the confidence coefficient. The realized interval (u1, u2) is the g confidence ^ In essence, interval. The terms u1 and u2 could possibly be functions of the estimator u. we can replace the empirical point estimator with an interval estimator that gives more information about the population parameter. The coefficient a ¼ (1 2 g) is called the significance level. In a two-sided confidence interval as in Eq. (18.4.1), it is usual to split the significance level a on both sides of the density. We will apply the confidence intervals to various parameters such as the mean and variance. We have already come across the basic ideas of confidence intervals in Section 7.9 and Examples 14.8.1 and 14.8.4. Example 18.4.1 In a presidential election, a news agency polled 1000 probable voters, and 470 of them said that they would vote Democratic while 460 of them said that they would vote Republican. The agency reported that the Democrat winning was 47% and the Republican winning was 46%, with a margin of error of +4%. What this means is that the true probability of the Democrat winning lies in the interval (51%, 43%) and that of the Republican lies in the interval (50%, 42%): P{0:43 , P(Democrat winning) , 0:51} ¼ x P{0:42 , P(Republican winning) , 0:50} ¼ x The conclusion arrived at is that the results are within the margin of error and hence the election is a dead heat. What is never mentioned is x, the probability of these results occurring! Confidence Interval for the Unknown Mean of Population (Known Variance). We P have already established in Eq. (18.3.10) that m^ X ¼ (1=n) ni¼1 Xi is an unbiased minimum variance point estimator for the mean mX. If n is large enough, we can assume that m^ X is Gaussian-distributed with variance s2X =n. We will find the confidence interval under the assumption that s2X is known. The random variable Z given by Z¼
m^ X mX pffiffiffi sX = n
(18:4:2)
is a standard Gaussian, and for a confidence coefficient g ¼ 1 2 a we can write P{Za=2 , Z z1a=2 } ¼ 1 a ¼ g or m^ m P za=2 , X pffiffiffiX z1a=2 ¼ g sX = n
(18:4:3)
where we have split the significance level a equally on both sides of the density function. The values za=2 ¼ P(Z a=2) and z1a=2 ¼ P(Z 1 a=2) for a confidence level of g are extracted from the Gaussian table (Table 18.4.1) for
18.4
Interval Estimation (Confidence Intervals)
365
TABLE 18.4.1 Gaussian Tables g%
P(Z z)
z
P(Z z)
z
99 98 95 90 85 80 75 70 65 60 55 50
0.005 0.01 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25
22.57583 22.32635 21.95996 21.64485 21.43953 21.28155 21.15035 21.03643 20.93459 20.84162 20.75542 20.67449
0.995 0.99 0.975 0.95 0.925 0.9 0.875 0.85 0.825 0.8 0.775 0.75
2.57583 2.32635 1.95996 1.64485 1.43953 1.28155 1.15035 1.03643 0.93459 0.84162 0.75542 0.67449
probabilities of a=2 and (1 a=2) respectively. Since the Gaussian curve is symmetric, za=2 ¼ z1a=2 . The z values for 95% confidence are z0.025 ¼ – 1.96 and z0.975 ¼ 1.96. The corresponding z values for 99% confidence are z0.005 ¼ – 2.576 and z0.995 ¼ 2.576 and for 90% confidence z0.05 ¼ – 1.645 and z0.95 ¼ 1.645. From Eq. (18.4.3) we have m^ X mX pffiffiffi z1a=2 ¼ 1 a P za=2 , sX = n from which it follows that za=2 sX z1a=2 sX P pffiffiffi , m^ X mX pffiffiffi ¼1a n n and finally
z1a=2 sX za=2 sX ¼1a P m^ X pffiffiffi , mX m^ X pffiffiffi n n
(18:4:4)
Equation (18.4.4)pis ffiffiffi to be interpreted pffiffiffias the probability that the random interval (m^ X z1a=2 sX = n, m^ X za=2 sX = n) will contain the true population mean mX is 100(1 2 a)%. The probability density of m^ X centered about the true mean mX and the confidence intervals are shown in Fig. 18.4.1. The 99% confidence interval is given by 2:576sX 2:576sX P m^ X pffiffiffi , mX m^ X þ pffiffiffi ¼ 0:99 (18:4:5) n n In an analogous manner, the 95% confidence interval is given by 1:96sX 1:96sX ¼ 0:95 P m^ X pffiffiffi , mX m^ X þ pffiffiffi n n
(18:4:6)
366
Estimation Theory
FIGURE 18.4.1
and the 90% confidence interval is given by 1:645sX 1:645sX P m^ X pffiffiffi , mX m^ X þ pffiffiffi ¼ 0:90 n n
(18:4:7)
It can be seen from Eqs. (18.4.5)– (18.4.7) that as the confidence level g decreases and conversely as the significance level a increases, the bounds become tighter. If we want to have 100% confidence or 0% significance, then the confidence interval is 1! The equations also show that as the number of samples n increases, the bounds also become tighter. The confidence level is usually fixed a priori at 95% or 90% before sampling. Confidence Interval for the Unknown Mean of Population (Unknown Variance). In the previous section we assumed that the variance is known. Since the true variance is known in very few cases, we use the estimated variance s^ 2X . In that case we have to find the distribution of the random variable T, given by T¼
m^ X mX pffiffiffi s^ X = n
(18:4:8)
which is a Student-t distribution given by Eq. (7.9.2) with n degrees of freedom. Equation (18.4.8) can rewritten as T¼
pffiffiffi (m^ X mX )=sX = n {½(n 1)(s^ X =sX )2 =(n 1)}1=2
(18:4:9)
pffiffiffi where the term (m^ X mX )=sX = n is a standard Gaussian, and we will show that the term n (n 1)s^ 2X X Xi m^ X 2 ¼ sX s2X i¼1 is chi-square-distributed with (n – 1) degrees of freedom. Adding and subtracting mX to (Xi m^ X ) in the numerator of the righthand side of the equation
18.4
Interval Estimation (Confidence Intervals)
367
above, we have n n X Xi m^ X 2 X Xi mX (m^ X mX ) 2 ¼ sX sX i¼1 i¼1 ( ) n X Xi mX 2 m^ X mX 2 2(Xi mX )(m^ X mX ) ¼ þ sX sX s2X i¼1 ( ) n X Xi mX 2 m^ mX 2 ¼ since n X sX sX i¼1 n X
(Xi mX ) ¼ n(m^ X mX )
(18:4:10)
i¼1
P The first term on the righthand side of Eq. (18.4.10), ni¼1 ½(Xi mX )=spXffiffiffi2 is chi-square-distributed with n degrees ofpfreedom. Since (m^ X mX )=(sX = n) is ffiffiffi Gaussian with mean 0 and variance sX = n, the second term m^ mX 2 m^ X mX 2 pffiffiffi n X ¼ sX sX = n P is chi-square-distributed with one degree of freedom. Hence ni¼1 ½(Xi m^ X )=sX 2 is chi-square-distributed with (n 2 1) degrees of freedom. Further, it can be shown that s^ 2X and m^ X are independent random variables. Hence, from Example 13.4.5, T in Eq. (18.4.8) is indeed Student-t-distributed with (n 2 1) degrees of freedom. We can now obtain the 100(1 – a)% confidence intervals for the population mean mX using the Student-t as follows. Analogous to Eq. (18.4.4), we can write (m^ X mX ) p ffiffi ffi P{ta=2 , T t1a=2 } ¼ P ta=2 , t1a=2 ¼ 1 a ¼ g s^ X = n from which it follows that pffiffiffi pffiffiffi P{ta=2 s^ X = n , (m^ X mX ) t1a=2 s^ X = n} ¼ 1 a ¼ g and finally pffiffiffi pffiffiffi P{m^ X t1a=2 s^ X = n , mX m^ X ta=2 s^ X = n} ¼ 1 a ¼ g
(18:4:11)
where ta=2 ¼ P(T a=2) and t1a=2 ¼ P(T 1 a=2) and ta=2 ¼ t1a=2 because of the symmetry of the Student-t density. From the Student-t table (Table 18.4.2), t1a=2 is extracted for n – 1 degrees of freedom and probability (1 2 a/2). Equation p (18.4.11) is to be read ffiffiffi pffiffiffi as the probability that the random interval (m^ X t1a=2 s^ X = n, m^ X ta=2 s^ X = n) includes the true population mean mX is equal to (1 2 a). These confidence limits are shown in Fig. 18.4.2 on a Student-t density with (n – 1) degrees of freedom.
Example 18.4.2 The tensile strength of six samples of a material is given by 226, 223, 225, 227, 224, and 222 lb. The estimated mean m^ X ¼ 224:5 lb. The standard error, s^ X ¼ 1:871 lb. Assuming a Gaussian distribution for the samples, the t0.95 value for (6 – 1) ¼ 5 degrees of freedom from Table 18.4.2 is given by t0.95 ¼ 2.01505. Hence the
368
Estimation Theory
TABLE 18.4.2 Student-t Tables with t-Values Shown v
FT (t) ¼ 0:95
FT (t) ¼ 0:975
FT (t) ¼ 0:995
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
6.31375 2.91999 2.35336 2.13185 2.01505 1.94318 1.89458 1.85955 1.83311 1.81246 1.79588 1.78229 1.77093 1.76131 1.75305 1.74588 1.73961 1.73406 1.72913 1.72472
12.70620 4.30265 3.18245 2.77645 2.57058 2.44691 2.36462 2.30600 2.26216 2.22814 2.20099 2.17881 2.16037 2.14479 2.13145 2.11991 2.10982 2.10092 2.09302 2.08596
63.65674 9.92484 5.84091 4.60409 4.03214 3.70743 3.49948 3.35539 3.24984 3.16927 3.10581 3.05454 3.01228 2.97684 2.94671 2.92078 2.89823 2.87844 2.86093 2.84534
lower confidence limit tL is pffiffiffi 1:871 ¼ 223:872 tL ¼ m^ X t0:95 s^ X = n ¼ 224:5 2:01505 6 and the upper confidence limit tU with t0.05 ¼ – t0.95 is pffiffiffi 1:871 ¼ 225:128 tU ¼ m^ X t0:05 s^ X = n ¼ 224:5 þ 2:01505 6 Hence we are 90% confident that the true mean lies in the interval (223.872, 225.128) lb. On the other hand, if we are given that the true standard deviation sX ¼ 1.871 lb, then we
FIGURE 18.4.2
18.4
Interval Estimation (Confidence Intervals)
369
use the Gaussian distribution for confidence intervals given by Eq. (18.4.6), and the lower and upper confidence intervals are pffiffiffi 1:871 ¼ 223:987 zL ¼ m^ X z0:95 sX = n ¼ 224:5 1:645 6 pffiffiffi 1:871 zU ¼ m^ X z0:05 sX = n ¼ 224:5 þ 1:645 ¼ 225:013 6 The confidence interval (223.987, 225.013) for the Gaussian is a little tighter than (223.872, 225.128) as obtained for the Student-t, showing that the Student-t density is flatter than the Gaussian as shown in Fig. 7.9.1. Example 18.4.3 In a probability examination for a class of 16 students, the following scores were recorded: X ¼ ½ 94
70 57
77
45
43
70 67
Estimated mean m^ X ¼
96
89
62
98 56
68
75
71
16 1 X Xi ¼ 71:125 16 i¼1
16 1 X (Xi 71:125)2 ¼ 283:183 15 i¼1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Standard error s^ X ¼ 283:183 ¼ 16:828
Estimated variance s^ 2X ¼
We have to find the 90% confidence limits for estimated mean m^ X ¼ 71:125. From the Student-t tables (Table 18.4.2) the t value for 15 degrees of freedom and 95% probability is 1.753. Hence the lower confidence limit is given by pffiffiffi 16:828 ¼ 63:75 tL ¼ m^ X t0:95 s^ X = n ¼ 71:125 1:753 16 and the upper confidence limit is pffiffiffi 16:828 ¼ 78:5 tL ¼ m^ X t0:05 s^ X = n ¼ 71:125 þ 1:753 16 Thus, we are 90% confident that the true mean will be in the interval (63.75, 78.5) or within +7.375 of the estimated mean 71.125. Confidence Interval for the Unknown Variance (Unknown Mean). We have already seen in Eq. (18.4.10) that the random variable (n 1)s^ 2X =s2X is chi-squaredistributed with (n 2 1) degrees of freedom. This density is not symmetric, and the interval estimate will not be centered at s2, unlike the Gaussian and Studentt densities. To determine the upper and lower confidence limits, we can write (n 1)s^ 2X 2 2 P xa=2,(n1) , x1a=2,(n1) ¼ 1 a ¼ g s2X from which it follows that ( ) x2a=2,(n1) x21a=2;(n1) 1 P , ¼g (n 1)s^ 2X s2X (n 1)s^ 2X
370
Estimation Theory
TABLE 18.4.3 Chi-Square Tables with u ¼ x2 Values Shown g ¼ 0.9 v 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
g ¼ 0.95
g ¼ 0.99
FU (u) ¼ 0:05 FU (u) ¼ 0:95 FU (u) ¼ 0:025 FU (u) ¼ 0:975 FU (u) ¼ 0:005 FU (u) ¼ 0:995 0.00393 0.10259 0.35185 0.71072 1.14548 1.63538 2.16735 2.73264 3.32511 3.94030 4.57481 5.22603 5.89186 6.57063 7.26094 7.96165 8.67176 9.39046 10.11701 10.85081
3.84146 5.99146 7.81473 9.48773 11.0705 12.59159 14.06714 15.50731 16.91898 18.30704 19.67506 21.02601 22.36199 23.68475 24.99576 26.29620 27.58709 28.86928 30.14351 31.41042
and finally
0.00098 0.05064 0.21580 0.48442 0.83121 1.23734 1.68987 2.17973 2.70039 3.24697 3.81575 4.40379 5.00875 5.62873 6.26214 6.90766 7.56419 8.23075 8.90652 9.59078
(
5.02389 7.37776 9.34840 11.14329 12.83250 14.44938 16.01276 17.53455 19.02277 20.48306 21.91996 23.33660 24.73555 26.11891 27.48836 28.84532 30.19099 31.52636 32.85231 34.16959
(n 1)s^ 2X (n 1)s^ 2X , s2X 2 P 2 x1a=2,(n1) xa=2,(n1)
0.00004 0.01003 0.07172 0.20699 0.41174 0.67573 0.98926 1.34441 1.73493 2.15586 2.60322 3.07382 3.56503 4.07467 4.60092 5.14221 5.69722 6.26480 6.84397 7.43384
7.87944 10.59663 12.83816 14.86026 16.74960 18.54758 20.27774 21.95495 23.58935 25.18818 26.75685 28.29952 29.81947 31.31935 32.80132 34.26719 35.71847 37.15645 38.58226 39.99685
) ¼g
(18:4:12)
where x21a=2,(n1) and x2a=2,(n1) are constants to be determined from the chi-square table (Table 18.4.3) for (n – 1) degrees of freedom at probability levels of 1 a=2 and a=2 respectively. Equation (18.4.12) is to be read as the probability that the random interval ! (n 1)s^ 2X (n 1)s^ 2X , x21a=2,(n1) x2a=2,(n1) includes the population variance s2X is equal to g ¼ (1 –a). These confidence limits are shown in Fig. 18.4.3 on a chi-square density with (n – 1) degrees of freedom. Example 18.4.4 We will find the 90% confidence interval for the sample variance s^ 2X ¼ 283:183 calculated in Example 18.4.3. From the chi-square table (Table 18.4.3), the x2 value for probability 0.05 and 15 degrees of freedom is 7.261, and the corresponding value for probability of 0.95 and 15 degrees of freedom is 24.996. Hence, from Eq. (18.4.12), the upper confidence limit is given by x2U ¼
(n 1)s^ 2X 15 283:183 ¼ 585:008 ¼ 7:261 x2a=2,(n1)
18.4
Interval Estimation (Confidence Intervals)
371
FIGURE 18.4.3
and the lower confidence limit is given by x2L ¼
(n 1)s^ 2X 15 283:183 ¼ 169:937 ¼ 2 24:996 x1a=2,(n1)
The corresponding 90% confidence limits for the standard error s^ X ¼ 16:828 are pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ( 169:937 ¼ 13:036, 585:008 ¼ 24:187). This result is to be interpreted as the probability of the standard deviation sX of the students’ scores will be in the interval (13.036, 24.187) is 90%. Confidence Interval for the Coefficients of Simple Linear Regression. Since the residual variance s^ 2w is chi-square-distributed with (n 2 2) degrees of freedom the estimated variances s^ 2a^ 1 and s^ 2a^ 2 for the regression coefficients a^ 1 and a^ 2 of the regression line y^ i ¼ a^ 1 þ a^ 2 xi , i ¼ 1, . . . ; n are also chi-square-distributed with (n 2 2) degrees of freedom as given in Eqs. (18.3.51). The terms a^ 1 and a^ 1 are unbiased a1 2, and hence the quantities (^ pffiffiffiffiffiffiffiffiffiffiffiestimators of a1 and paffiffiffiffiffiffiffiffiffiffiffi a1 )=(s^ a^ 1 = n 2) and (^a2 a2 )=(s^ a^ 2 = n 2) are Student-t-distributed with (n 2 2) degrees of freedom. From Eqs. (18.4.11), we can form equations for 100(1 2 a)% confidence intervals (^a1 a1 ) P ta=2,(n2) , t1a=2,(n2) ¼ 1 a ¼ g s^ a^ 1 (18:4:13) (^a2 a2 ) t1a=2,(n2) ¼ 1 a ¼ g P ta=2,(n2) , s^ a^ 2 where t1a=2,(n2) is obtained from the t table (Table 18.4.2) for (n 2 2) degrees of freedom and probability (1 2 a/2). Equation (18.4.13) can be stated more formally as P{^a1 t1a=2,(n2) s^ a^ 1 , a1 a^ 1 ta=2,(n2) s^ a^ 1 } ¼ 1 a P{^a2 t1a=2,(n2) s^ a^ 2 , a1 a^ 2 ta=2,(n2) s^ a^ 2 } ¼ 1 a
(18:4:14)
and hence Confidence interval for a1 ¼ (^a1 t1a=2,(n2) s^ a^ 1 , a^ 1 ta=2,(n2) s^ a^ 1 ) (18:4:15) Confidence interval for a2 ¼ (^a2 t1a=2,(n2) s^ a^ 2 , a^ 2 ta=2,(n2) s^ a^ 2 )
372
Estimation Theory
Example 18.4.5 We will now find 90% confidence intervals for the estimated regression coefficients of Example 18.3.3. The estimated regression coefficients are given by a^ 1 ¼ 1:574 and a^ 2 ¼ 0:404, with estimated variances s^ 2a^ 1 ¼ 0:0704 s^ 2a^ 2 ¼ 0:0699. The standard errors for a^ 1 and a^ 2 are s^ a^ 1 ¼ 0:265 and s^ a^ 2 ¼ 0:264. From the t table (Table 18.4.2), for 18 degrees of freedom and probability of 0.95, t0.95,18 ¼ 1.7341. From Eq. (18.4.15) we can compute the 90% confidence interval for a^ 1 as La^ 1 1:115 ¼ 1:574 1:734 0:265 ¼ Ua^ 1 2:034 and the 90% confidence interval for a^ 2 is La^ 2 1:115 ¼ 0:404 1:734 0:264 ¼ Ua^ 2 0:054 Confidence Region for Simple Linear Regression Line. line is given as follows: y^ i ¼ a^ 1 þ a^ 2 xi ,
The estimated regression
i ¼ 1, . . . , n
(18:3:49)
Since y^ i is a Gaussian-distributed unbiased estimator of yi with estimated variance pffiffiffiffiffiffiffiffiffiffiffi s^ 2y^ i given by Eq. (18.3.54), the quantity (^yi yi )=(s^ y^ i = n 2) is Student-t-distributed with (n – 2) degrees of freedom. Hence, we can form equations for 100(1 – a)% confidence intervals as y^ i yi P ta=2,(n2) , t1a=2,(n2) ¼ 1 a ¼ g, i ¼ 1, . . . , n (18:4:16) s^ y^ i where t1a=2,(n2) is obtained from the t table (Table 18.4.2) for (n – 2) degrees of freedom and probability (1 – a/2). Equation (18.4.16) can be rewritten as P{^yi t1a=2,(n2) s^ y^ i , yi y^ i ta=2,(n2) s^ y^ i } ¼ 1 a,
i ¼ 1, . . . , n (18:4:17)
and the lower bound of the 100(1 – a)% confidence region for yi is given by Ly^ i ¼ y^ i t1a=2,(n2) s^ y^ i
(18:4:18)
with the upper bound of the region given by Uy^ i ¼ y^ i ta=2,(n2) s^ y^ i
(18:4:19)
Example 18.4.6 In Example 18.4.5 we will find the 90% confidence region for the regression line given by y^ i ¼ 1:574 þ 0:404xi ,
i ¼ 1, . . . , 20
From Example 18.3.3 the variance of the regression line is given by 1 (xi 0:905)2 þ s^ 2y^ i ¼ 0:263 , i ¼ 1, . . . , 20 20 3:7665 This value of s^ 2y^ i can be substituted in Eq. (18.4.18) to obtain the lower bound of the 90% confidence region 1=2 1 (xi 0:905)2 þ , i ¼ 1, . . . , 2 Ly^ i (xi ) ¼ 1:574 þ 0:404xi 1:7341 0:263 20 3:7665
18.5
Hypothesis Testing (Binary)
373
FIGURE 18.4.4
and the upper bound of the region can be obtained from Eq. (18.4.19): 1=2 1 (xi 0:905)2 þ , i ¼ 1, . . . , 20 Uy^ i (xi ) ¼ 1:574 þ 0:404xi 1:7341 0:263 20 3:7665 The confidence region is shown in Fig. 18.4.4 along with the regression line y^ i and the data points.
B 18.5
HYPOTHESIS TESTING (BINARY)
We have discussed some estimation problems in the previous sections. However, many probability models require us to make decisions about populations on the basis of limited knowledge. For example, a physician has to make a decision based on knowledge of her patient whether to give a particular drug. Usually this decision is binary in nature, that is, to give or not to give. Thus, we have a decision problem as opposed to an estimation problem. Such decision problems come under the category of hypothesis testing. We postulate a carefully formed assertion based on available information. This assertion is called the null hypothesis, H0. If we are interested only in accepting it or rejecting it at a significance level a, then it is called significance testing. On the other hand, we may be interested in postulating an alternate hypothesis H1 and finding methods of accepting H0 or H1. This is called hypothesis testing. For example, we may be interested in testing the null hypothesis H0 that the mean time between failures u (MTBF) of a memory chip is u0 hours. The alternate hypothesis may be u ¼ u1 or u, u0. We are not particularly interested in the alternate hypothesis u . u0 because it does not matter whether the chip overperforms. On the other hand, in the communication receiver we can hypothesize that the noise variance is u0. We are now interested in the alternate hypothesis u . u0 because we do not want the variance to be too high, while we are not interested in u , u0 because it does not affect the performance for low variances. The experiment on which we test a null hypothesis is based on an n random vector X, fXi, i ¼ 1, . . . , ng from a population as in the estimation problem. The n random vector will span an n-dimensional Euclidean space R n. We divide this n-space of observations into two mutually exclusive regions, R(H0) and R(H1). If the observed vector x, fxi, i ¼ 1, . . . , ng, called the test statistic, lies in the space R(H1), we reject the null
374
Estimation Theory
TABLE 18.5.1 True Situation H0 H0 H0 H0
true, H1 false true, H1 false false, H1 true false, H1 true
Accept or Reject
Decision Type
Accept H0 Reject H0 Accept H0 Reject H0
Correct decision—no error Type I error—a Type II error—b Correct decision—no error
hypothesis H0. On the other hand, if it falls in the region R(H0), we may not reject H0. The region R(H0) is called the acceptance region, and R(H1) is called the critical region or rejection region. Whether we accept or reject the null hypothesis, there is always the possibility of errors. The error created when H0 is rejected when it is true is called type I error and is denoted by a, which is also known as the significance level. Other names for this error are false alarm and producer’s risk. The error created when H0 is accepted when it is false is called type II error and is denoted by b. Other terms for this error are missed alarm and consumer’s risk. The four possibilities, two of which are correct decisions and the other two, erroneous decisions, are shown in Table 18.5.1. A simple hypothesis is one in which the parameters are specified exactly. If they are not, it is called a composite hypothesis. A null hypothesis is almost always simple. For example, H0 ¼ u0 is simple whereas H1 . u0 is composite. In testing hypotheses we usually control the significance level a a priori and try to minimize the type II error b. Example 18.5.1 (Significance Testing) A pharmaceutical company wants to test the effectiveness of the new antihypertensive drug that they are developing. A group of n ¼ 25 volunteers are selected with a mean systolic pressure of m25 ¼ 150 mmHg with a standard deviation of s25 ¼ 25 mmHg. The company wants to see whether the new drug can bring the systolic pressure below 140 mmHg. We have to design a test to determine whether the drug is effective at a significance level of a ¼ 0.01. We will assume that the systolic pressure is a Gaussian-distributed random variable X with mean mX ¼ 150 and standard deviation sX ¼ 25. Hence the sample mean m^ 25 (X) of systolic pressures afterptaking the drug is Gaussian-distributed with mean 150 and standard ffiffiffiffiffi deviation sm^ X ¼ 25= 25 ¼ 5 as shown in Fig. 18.5.1. We have to form the null
FIGURE 18.5.1
18.5
Hypothesis Testing (Binary)
375
hypothesis H0 carefully. If we choose the null hypothesis that the sample mean m^ 25 (X) after taking the drug is the same as the mean value of 150, then the drug has minimal effect on the systolic pressure. If we now choose a region R such that P{m^ 25 (X) [ R} ¼ P{m^ 25 (X) ra } ¼ 0:01 and the null hypothesis is rejected at a significance level of 1%, then we can conclude that the drug is effective in controlling systolic blood pressure. From Eq. (18.4.2) the test statistic is Z¼
m^ X (X) mX m^ X (X) mX pffiffiffi ¼ sm^ X sX = n
and we can write a one-sided equation: m^ X mX m^ X 150 p ffiffi ffi za ¼ a ¼ 0:01 P za ¼ P 5 sX = n Referring to Table 18.4.1 the value of za corresponding to a probability of 0.01 is 22.326. Hence m^ X 150 ¼ 2:326 5
or m^ X ¼ 150 5 2:326 ¼ 138:37
so that ra ¼ 138.37 as shown in Fig. 18.5.1. Thus, we can reject the hypothesis H0 that the drug is ineffective in controlling the systolic blood pressure if the sample mean of the pressure measurements drops below 139. On the other hand, if we increase the significance level to 2.5%, then z0.025 ¼ 21.96 and m^ X 150 ¼ 1:96 5
or
m^ X ¼ 150 5 1:96 ¼ 140:2
and we cannot reject the hypothesis at the higher significance level of 2.5% and conclude that the drug is ineffective at that level. Thus, the significance level has to be fixed before the test begins. We shall now consider several cases of testing hypotheses. The form of the density function of a random variable X is a known function fX (x,u) that depends on a parameter u. Case 1: H0 ¼ u0 and H1 ¼ u1. Assuming a Gaussian random variable, consider a simple hypothesis H0 ¼ u0 against a simple alternate H1 ¼ u1. The decision to accept or reject H0 is dependent on the test statistic x obtained from the population. The probability densities fX(x,u0) and fX(x,u1) and the error regions a and b are shown in Fig. 18.5.2. If x , c, the null hypothesis H0 will be accepted, and if x . c, H0 will be rejected. The critical region R(H1) is x . c. The type I error a depends on the hypothesis under test H0, and the type II error b depends on both H0 and the alternate hypothesis H1. We can also see from Fig. 18.5.2 that we cannot minimize both types of error simultaneously since decreasing a increases b and vice versa. Case 2: H0 ¼ u0 and H1 ¼ u . u0 (One-Sided Test). In this case we have a simple hypothesis H0 ¼ u0 and a composite alternate H1 ¼ u . u0. The acceptance region for H0 is x , c. Here u ranges from u0 to 1 and the error probability b
376
Estimation Theory
FIGURE 18.5.2
will be a function of u. The Gaussian probability densities along with the errors a and b(u) are shown in Fig. 18.5.3. The acceptance and rejection regions are also shown in figure. The critical region will still be x . c, but the type II error function b(u) will depend on the closeness of u to u0. The function b(u) is called the operating characteristic (OC) (Fig.18.5.4) of the test. The OC shows the probability of a wrong decision. It can be visualized by sliding the density function fX(x,u) under H1 to the left starting from u0 and integrating it from 1 to c, or ðc ðc 2 1 pffiffiffiffiffiffi e(1=2)½(xu)=s dx b(u) ¼ fX (x,u)dx ¼ (18:5:1) 2p:s 1 1 The complementary function P(u) ¼ 1 b(u) is called the power of the test. It shows the probability of a correct decision. The operating characteristic (OC) is shown in Fig. 18.5.4. Case 3: H0 ¼ u0 and H1 = u0 (Two-Sided Test). Here, we have a simple hypothesis H0 ¼ u0 and a composite alternate H1 ¼ {u = u0 g. The acceptance region is c1 , x , c2. The significance level a is split between the two tails of the
FIGURE 18.5.3
18.5
Hypothesis Testing (Binary)
377
FIGURE 18.5.4
FIGURE 18.5.5
FIGURE 18.5.6
density under H0 so that P{m(x) ^ , za=2 } ¼ c1 and P{m(x) ^ , z1a=2 } ¼ c2 . Here u ranges from c1 to c2, and the error probability b will be a function of u. The Gaussian probability densities along with the errors a and b(u) are shown in Fig. 18.5.5.
378
Estimation Theory
The critical region is x , c1 and x . c2, but the type II error function b(u) (OC) extends from 1 to 1 (Fig. 18.5.6) and the value will depend on the closeness of u to u0. It can again be visualized by sliding the density function fX(x, u) under H1 to the left, starting from c1 and integrating it from c1 to c2: ð c2 ð c2 2 1 pffiffiffiffiffiffi e(1=2)½(xu)=s dx b(u) ¼ fX (x,u)dx ¼ (18:5:2) 2p:s c1 c1 The acceptance region is also shown in Fig. 18.5.5 and the two-sided OC curve, in Fig. 18.5.6. Procedure for Hypothesis Testing
The following procedures are established for credible testing of hypotheses: 1. Null hypothesis H0 must be carefully formulated. 2. Define the experiment to be performed. 3. Choose a proper test statistic. 4. Establish the significance level a. 5. Choose a value for type II error b. 6. With a and b specified, determine a suitable sample size n. We will now apply hypothesis testing to the mean and variance of a population. We will have n realizations {x1 , . . . , xn ) of a Gaussian random variable P X whose mean m we want to hypothesize. The sample mean m^ is, as usual, m^ ¼ (1=n) n xi , and the variance of the sample mean s^ 2 ¼ s2 =n. The density of m^ will be given by pffiffi 1 (1=2)½(xm)=(s= n)2 fm^ (x) ¼ pffiffiffiffiffiffi (18:5:3) pffiffiffi e 2p s= n These quantities will be used to form the test statistic.
Mean m—known Variance s
We will now test the hypothesis that the mean m is H0 ¼ m0 against the alternates: (1) H1 ¼ m , m0 , (2) H1 ¼ m . m0 , and (3) H1 ¼ m = m0 . The test statistic we will use is the random variable Z¼
m^ m0 pffiffiffi s= n
(18:5:4)
with a significance level of a. Under the hypothesis H0, the random variable Z is Gaussian with zero mean and unit variance. Note that za for which P(Z za ) ¼ a will be negative for a , 0:5 and z1a for which P(Z z1a ) ¼ 1 a will be positive, and if the pdf is symmetric, then za ¼ z1a . 1. H1 ¼ m , m0 : Accept H0 if Z . za. Or, from Eq. (18.5.4) Z¼
m^ m0 pffiffiffi . za s= n
or
pffiffiffi m^ . m0 þ za s= n
(18:5:5)
pffiffiffi The acceptance region R(H pffiffiffi 0 ) ¼ {m^ . m0 þ za s= n} and thepffiffiffirejection region R(H1 ) ¼ {m^ , m0 þ za s= n}. The critical point c ¼ m0 þ za s= n} The OC curve
18.5
Hypothesis Testing (Binary)
is obtained from Eq. (18.5.1): ð1 ð1 pffiffi 1 (1=2)½(xm)=(s= n)2 pffiffiffiffiffiffi b(m) ¼ fm^ (x, m)dx ¼ dx pffiffiffi e 2p s= n c c 1 1 m0 m p ffiffi ffi p ffiffi ffi 1 erf ¼ þ za 2 2 s= n
379
(18:5:6)
where ðx erf(x) ¼
2 2 pffiffiffiffi ej dj p 0
(18:5:7)
2. H1 ¼ m . m0 : In this case we accept H0 if Z . z12a. Or Z¼
m^ m0 pffiffiffi , z1a s= n
pffiffiffi or m^ . m0 þ z1a s= n
(18:5:8)
pffiffiffi The acceptance region R(Hp ^ , m0 þ z1a s= n} and the rejection region 0 )ffiffiffi¼ {m pffiffiffi R(H1 ) ¼ {m^ . m0 þ z1a s= n}. The critical point c ¼ m0 þ z1a s= n The OC curve is obtained from Eq. (18.5.1): ðc ðc pffiffi 1 (1=2)½(xm)=(s= n)2 pffiffiffiffiffiffi b(m) ¼ fm^ (x, m)dx ¼ dx pffiffiffi e 2p s= n 1 1 ¼
1 1 m0 m pffiffiffi þ z1a 1 þ erf pffiffiffi 2 2 s= n
(18:5:9)
where erf(x) is given by Eq. (18.5.7) 3. H1 ¼ m = m0 : In the previous two cases we had one-sided hypothesis testing, whereas here we will have two-sided hypothesis testing. As seen previously, the significance level a is split evenly between the two tails of the density under H0 so that the probability is a/2 on each tail. We now accept H0 if za=2 , Z , z1a=2 , or za=2 ,
m^ m0 pffiffiffi , z1a=2 s= n
or
pffiffiffi pffiffiffi m0 þ za=2 s= n , m^ , m0 þ z1a=2 s= n (18:5:10)
pffiffiffi pffiffiffi The acceptance region R(H0 ) ¼ {m0 þ za=2 s= n} and the pffiffiffi n , m^ , m0 þ z1a=2pffiffis= ffi rejection region R(H1 ) ¼ {m^ p ,ffiffiffim0 þ za=2 s= n m^ . m0 p þffiffizffi1a=2 s= n}. The critical points are c1 ¼ m0 þ za=2 s= n and c2 ¼ m0 þ z1a=2 s= n. The OC curve is obtained from Eq. (18.5.2): ð c2 ð c2 pffiffi 1 (1=2)½(xm)=(s= n)2 pffiffiffiffiffiffi fm^ (x,m)dx ¼ dx b(m) ¼ pffiffiffi e 2p s= n c1 c1 1 1 m m 1 m m ¼ erf pffiffiffi 0 pffiffiffi þ z1a=2 erf pffiffiffi 0 pffiffiffi þ za=2 (18:5:11) 2 2 s= n 2 s= n where erf(x) is as given by Eq. (18.5.7). Mean m—Unknown Variance s2
The procedure with an unknown variance is similar to the one with the known variance discussed earlier. We replace the unknown variance with the estimated variance
380
Estimation Theory
s^ 2X and use the Student-t distribution for analysis. The test statistic is m^ m0 pffiffiffi T¼ s= ^ n
(18:5:12)
and this is Student-t-distributed with (n 2 1) degrees of freedom. The critical regions for the three cases discussed earlier are 1. H1 ¼ m , m0 Acceptance region: Rejection region: 2. H1 ¼ m . m0
t(n1),a s^ pffiffiffi R(H0 ) ¼ m^ . m0 þ n t(n1),a s^ pffiffiffi R(H1 ) ¼ m^ , m0 þ n
(18:5:13)
Acceptance region: Rejection region:
t(n1),ð1aÞ s^ pffiffiffi R(H0 ) ¼ m^ , m0 þ n t(n1),ð1aÞ s^ pffiffiffi R(H1 ) ¼ m^ . m0 þ n
(18:5:14)
3. H1 ¼ m = m0
t(n1),a=2 s^ t(n1),(1a=2) s^ pffiffiffi pffiffiffi Acceptance region: R(H0 ) ¼ m0 þ , m^ , m0 þ n n (18:5:15) ) t(n1),(a=2) s^ t(n1),(1a=2) s^ pffiffiffi pffiffiffi Rejection region: R(H1 ) ¼ m^ , m0 þ ; m^ . m0 þ n n (
(18:5:16) To determine the OC curves in these three cases we have to integrate a noncentral t density, and this is beyond the scope of our discussion. However, for large n, the t distribution becomes Gaussian and the OC curves can be approximated by Eqs. (18.5.6), (18.5.9), and (18.5.11). Example 18.5.2 The following diastolic blood pressure measurements were recorded for a patient on 10 different occasions. The pressure measurements are assumed to be Gaussian-distributed: BP measurements
78
85
88
85
78
86
79
82
80
81
We want to find whether these measurements support the hypothesis that the pressure is less than 80 at the significance levels of (1) 1%, (2) 2.5%, and (3) 5%. This problem is a little different from what we discussed earlier because both hypotheses P 1 are composite. The estimated mean value m^ ¼ 10 (78 þ þ 81) ¼ 82:2 and the estimated standard deviation s^ ¼ 3:584. We choose the statistic given by Eq. (18.5.12) m^ m0 pffiffiffi T¼ s= ^ n which is Student-t-distributed. The t values for 1%, 2.5%, and 5% significance levels for (10 2 1) ¼ 9 degrees of freedom from the Student-t table (Table 18.4.2) are 2.8214,
18.5
Hypothesis Testing (Binary)
381
2.2622, and 1.8331 respectively. We can now formulate the composite hypotheses as (1) null hypothesis H0 : m , m0 ¼ 80; and (2) alternate hypothesis H1 : m m0 ¼ 80: The acceptance regions from Eq. (18.5.14) are pffiffiffi 1% significance: R(H0 ) ¼ {m^ , m0 þ t(n1), (1a) s= ^ n} 3:5839 ¼ 80 þ 2:8214 pffiffiffiffiffi ¼ 83:2 10 pffiffiffiffi 2.5% significance: R(H0 ) ¼ m^ , 80 þ 2:2622 3:5839 ¼ 82:564 10 pffiffiffiffi 5% significance: R(H0 ) ¼ m^ , 80 þ 1:8331 3:5839 ¼ 82:078 10 Since the estimated mean is m^ ¼ 82:2 , 82:564 , 83:2, we conclude that the hypothesis with the diastolic pressure is 80 at significance levels of 1% and 2.5% can be accepted. It cannot be accepted at the 5% level because m^ ¼ 82:2 . 82:078. We will not calculate the OC curve b(m) since this involves noncentral Student-t distribution. Example 18.5.3 An energy specialist recommends that a homeowner could save at least $15 a month in utilities if energy appliances were upgraded. Before the upgrade was installed, the mean monthly utility expense m0 was $255 with standard deviation s ¼ $17. After the upgrade, the mean monthly expenses (in U.S. dollars) for a 10-year period is shown below: 243 252 246 262 244 219 228 212 264 260 We have to test whether the upgrade has saved $15 a month at significance levels of (1) 1%, (2) 2.5%, (3) 5%. We will assume that the utility expenses are Gaussian-distributed and formulate the null hypothesis H0 as mean monthly expense m0 ¼ $255 and the alternate hypothesis H1 as mean monthly expense m1 ¼ $240. If H0 is rejected and H1 is accepted at significance levels of a ¼ 0.01,0.025,0.05, then the homeowner would have saved $15 a month. The 1 estimated mean value m^ of the average monthly expense m^ ¼ 10 ½243 þ þ 260 ¼ 243 with s given as 17. The density functions of m^ for H0 and H1 are given by pffiffiffiffi 2 1 pffiffiffiffiffi e(1=2)½(x255)=(17= 10) fm^ (x j H0 ) ¼ pffiffiffiffiffiffi 2p 17= 10 pffiffiffiffi 2 1 pffiffiffiffiffi e(1=2)½(x240)=(17= 10) fm^ (x j H1 ) ¼ pffiffiffiffiffiffi 2p 17= 10 and are shown in Fig. 18.5.7. The test statistic is chosen as the normalized Gaussian m^ m0 m^ 255 pffiffiffiffiffi pffiffiffi ¼ Z¼ s= n 17= 10 and the simple hypotheses H0: m ¼ m0 ¼ 255 and H1: m ¼ m1 ¼ 240 at significance levels of 1%, 2.5%, and 5% can be tested. The corresponding z values from the Gaussian table (Table 18.4.1) are z0.01 ¼ 22.326, z0.025 ¼ 21.96, and z0.05 ¼ 21.645 respectively. The acceptance regions from Eq. (18.5.6) are pffiffiffi 1% significance: R(H0 ) ¼ {m^ . m0 þ za s= n} pffiffiffiffiffi ¼ 255 2:326 (17= 10) ¼ 242:49
382
Estimation Theory
FIGURE 18.5.7
pffiffiffi 2:5% significance: R(H0 ) ¼ {m^ . m0 þ za s= n} pffiffiffiffiffi ¼ 255 1:96 (17= 10) ¼ 244:47 pffiffiffi 5% significance: R(H0 ) ¼ {m^ . m0 þ za s= n} pffiffiffiffiffi ¼ 255 1:645 (17= 10) ¼ 246:16 pffiffiffi The critical point c ¼ m0 þ za s= n. The 5% significance region and the estimated mean m^ ¼ 243 are shown in Fig. 18.5.7. Since the estimated mean is m^ ¼ 243 . 242:5, we conclude that the null hypothesis m0 ¼ $255 can be accepted at the 1% level with consequent no average monthly savings of $15. On the other hand, m^ ¼ 243 , 244:47 , 246:16, and the null hypothesis is rejected at the 2.5% and 5% levels and the alternate hypothesis m1 ¼ 240 is accepted, resulting in an average monthly savings of $15. The OC curve b(m,za) for significance level a is obtained from Eq. (18.5.6) as 1 1 m0 m 1 1 255 m pffiffiffi þ za 1 erf pffiffiffi 1 erf pffiffiffi þ za b(m, za ) ¼ ¼ 2 2 2 s= n 2 5:4259 The type II errors for a ¼ 0.01, 0.025, 0.05 are obtained by substituting m ¼ 240 and z0.01 ¼ 22.326, z0.025 ¼ 21.96, z0.05 ¼ 21.645 in the equation above. Thus, b(240, 22.326) ¼ 0.3214, b(240, 21.96) ¼ 0.2032, b(240, 21.645) ¼ 0.1261. The operating characteristic curves shown in Fig. 18.5.8 are obtained from plotting b(m,za) for 1%, 2.5%, and 5% values of za. Number of Samples n for Given a and b. In testing hypotheses the usual practice is to fix the type I error a at a value of 2.5% or 5% and minimize the type II error b. The only way to minimize b is to increase the sample size n. For the case of H0: m ¼ m0 and H1 ¼ m , m0, the critical point c for Gaussian distributions is found to be pffiffiffi c ¼ m0 þ za s= n
(18:5:17)
If the allowable b is also specified, we can then obtain the required number of samples n.
18.5
Hypothesis Testing (Binary)
383
FIGURE 18.5.8
Referring to Fig. 18.5.9, we require n to be such that ð1 pffiffi 1 (1=2)½(xm1 )=(s= n)2 pffiffiffiffiffiffi dx b pffiffiffi e 2p s= n c pffiffiffi Substituting j ¼ (x m1 )=(s= n) in this equation, we obtain ð1 2 1 pffiffiffiffiffiffi e(1=2)j dj b cm1 2p pffi s= n
(18:5:18)
(18:5:19)
If the integral in Eq. (18.5.19) is to be b, then the lower limit c m1 pffiffiffi . z1b s= n
(18:5:20)
where z12b is obtained from the Gaussian tables for a probability of b. Substituting for c from Eq. (18.5.17) in Eq. (18.5.20) and simplifying, we have m0 m1 pffiffiffi . z1b za s= n
FIGURE 18.5.9
384
Estimation Theory
FIGURE 18.5.10
Thus the sample size n is given by n.
s2 (z1b za )2 (m0 m1 )2
(18:5:21)
Example 18.5.4 We will use the data from Example 18.5.2 and find the required sample size for a ¼ 0.05, and we are also given s ¼ 17, m0 ¼ 255, and m1 ¼ 240. As the number of samples n is increased, the critical point c increases and b decreases and we stop at the value of n that gives the desired value of b. In this example, for n ¼ 5,10,15 we obtain c ¼ 242.49, 246.16, 247.78 and b ¼ 0.3715, 0.1261, 0.0382 respectively. These are shown in Fig. 18.5.10. If we choose the value of b ¼ 0.1261 from the previous example, we should get n ¼ 10. From Gaussian tables z0.05 ¼ 21.645 and z12b ¼ z0.8739 ¼ 1.145. Substituting these values in Eq. (18.5.21), we obtain n . ½172 (1:145 þ 1:645)2 =(255 240)2 ¼ 9:9982 or n ¼ 10. This corresponds to the 10 samples of Example 18.5.2.
B 18.6.
BAYESIAN ESTIMATION
The previous approaches of estimating a parameter u in the probability density f (x,u) were based solely on the random samples fX1, . . . , Xng that are incorporated in the test statistic. This methodology is called the classical or the objective approach. However, in some problems we may have prior information about u that can be used to refine its estimate. For example, the classical estimate of the probability u in a binomial distribution b(k;n,u), where k is the number of successes in n trials, is u^ ¼ k=n. We can then view u as the realization of a random variable Q whose density function f (u) is the prior information. The density of X will be a conditional density f (x j u). The joint density of the sample fX1, . . . , Xng and the parameter Q can be given in terms of the conditional density as f (x1 , . . . , xn ,u) ¼ f (x1 , . . . , xn j u)f (u)
(18:6:1)
18.6. Bayesian Estimation
385
Using Bayes’ theorem, we can obtain the conditional density f (u j x1 , . . . , xn ) as f (u j x1 , . . . ; xn ) ¼ Posterior
f (x1 , . . . , xn , u) f (x1 , . . . , xn , ju) ¼ f (u) f (x1 , . . . , xn ) f (x1 , . . . , xn ) Prior
where the marginal density f (x1 , . . . , xn ) is given by ð1 f (x1 , . . . , xn ) ¼ f (x1 , . . . , xn ,u) du
(18:6:2)
(18:6:3)
1
The density f (u j x1 , . . . , xn ) is called the posterior density. What we have accomplished is modifying the prior density f(u) by knowledge of the joint density of the random samples fX1, . . . , Xng to the posterior density. This method of estimation is called the subjective approach to estimation or Bayesian estimation. It is used in medical imaging problems, which are discussed in Chapter 23. We will now present Bayes’ estimation of the probability p of an event where p is a realization of a random variable X with probability density function fX( p) whose range is [0,1]. Prior estimates of p can be obtained from ð1 (18:6:4) p^ ¼ pfX ( p)dp 0
To improve on the estimate of p, we conduct an experiment of tossing a die n times and observing the number of aces to be k. We can apply Bayes’ theorem from Eq. (8.3.4) and write the posterior density as fX (p j B) ¼ Ð 1 0
P(B j X ¼ p)fX ( p) P(B j X ¼ p)fX ( p)dp
where B ¼ fk aces in n trialsg. From binomial probability, we obtain n k p (1 p)nk P(B j X ¼ p) ¼ k Substituting Eq. (18.6.6) into Eq. (18.6.5), we obtain n k p (1 p)nk fX (p) k fX (p j B) ¼ Ð1 n nk k fX (p)dp 0 k p (1 p)
(18:6:5)
(18:6:6)
(18:6:7)
The updated estimate of p can be obtained by substituting fX ( p j B) from Eq. (18.6.7) for fX ( p) in Eq. (18.6.4), and the result is ð1 p^ post ¼ pfX (p j B)dp (18:6:8) 0
If we now assume that fX( p) is uniformly distributed in f0,1] instead of a general distribution in the range [0,1], Eq. (18.6.7) can be simplified. The integral ð1 m!(n k)! (18:6:9) pm (1 p)nk dp ¼ (n þ m k þ 1)! 0 can be shown to be true using mathematical induction.
386
Estimation Theory
Substituting fX( p) ¼ 1 and from Eq. (18.6.9), we can evaluate fX(B) as ð1 n! k!(n k)! 1 n ¼ fX (B) ¼ pk (1 p)nk 1dp ¼ k k!(n k)! (n þ 1)! n þ 1 0
(18:6:10)
we can express the conditional density fX( p j B) as follows: n! pk (1 p)nk 1 (n þ 1)! k k!(n k)! p (1 p)nk ¼ fX (p j B) ¼ 1 k!(n k)! (n þ 1)
(18:6:11)
The posterior estimate for p is obtained from Eq. (18.6.8) as ð (n þ 1)! 1 kþ1 p (1 p)nk dp p^ post ¼ k!(n k)! 0 and from Eq. (18.6.9) p^ post is given by p^ post ¼ Example 18.6.1
(n þ 1)! (k þ 1)!(n k)! k þ 1 ¼ k!(n k)! (n þ 2)! nþ2
(18:6:12)
We have to estimate the parameter Q in the following problem Yi ¼ Q þ Wi ,
i ¼ 1, . . . , n
where fWig are independent identically distributed zero mean Gaussian random variables with variance s2W and Q is also a Gaussian-distributed random variable with mean mQ and variance s2Q . We want to find the Bayesian estimate m^ Q of Q. From Eq. (18.6.2), the conditional density of Q conditioned on the n-vector random variable Y can be given as f (y, u) f (y j u)f (u) f (y j u)f (u) ¼ ¼ Ð1 f (y) f (y) 1 f (y j u)f (u)du Ð1 Ð1 Since 1 f (u j y)du ¼ ½1=f (y) Ð 1 1 f (y j u)f (u)du ¼ 1, the factor f(y) is independent of u and normalizes the integral 1 f (y j u)f (u)du to 1. The joint density function f (y j Q ¼ u) is governed by the joint density of W, and hence f (u j y) ¼
f (y j u) ¼
n Y
n Y
2 1 pffiffiffiffiffiffi e(1=2)½(yi u)=sW 2psW i¼1 Pn 1 (1=2) ½(yi u)=sW 2 i¼1 ¼ e n=2 n (2p) sW
f (yi j u) ¼
i¼1
with 2 1 f (u) ¼ pffiffiffiffiffiffi e(1=2)(u=sQ ) 2psQ
Multiplying f (y j u) and f (u), we have " 2 #) n 1 X yi u 2 u f (y j u)f (u) ¼ f (y, u) ¼ exp þ 2 i¼1 sW sQ (2p)(nþ1)=2 snW sQ 1
(
18.6. Bayesian Estimation
The factor
n P
387
(yi u)2 can be written as,
i¼1 n X
n X
i¼1
i¼1
(yi u)2 ¼
n X
( yi m^ Y þ m^ Y u)2 ¼
(yi m^ Y )2 þ n(m^ Y u)
i¼1
since the cross product vanishes. Substitution of this result in f (y,u) yields ( " #) n 1 1 X yi m^ Y 2 f (y,u) ¼ exp 2 i¼1 sW (2p)(nþ1)=2 snW sQ 1 n 1 2 2 exp (m^ u) þ 2 u 2 s2W Y sQ
1 n 1 2 2 ¼ C exp (m^ u) þ 2 u 2 s2W Y sQ
2 2 1 n 2 2 nsQ þ sW ¼ C exp (m^ 2m^ Y u) þ u 2 s2W Y s2W s2Q
ns2Q m^ 2Y 1 ns2Q þ s2W 2 2ns2Q m^ Y u ¼ C exp u 2 þ 2 s2W s2Q nsQ þ s2W ns2Q þ s2W where C is a function of only the sample values fyig and the parameters. Completing the squares in u in the exponent and after some algebra, there results 8 32 9 2 2 > > ns m ^ > > Y > > > 6u 2 Q 2 7 > = < 7 ns þ s 16 W7 Q f (y,u) ¼ C exp 6 sW s Q 7 > > 26 > 4 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 5 > > > > > ; : ns2 þ s2 W
Q
exp
1 nm^ 2Y 2 2 nsQ þ s2W
¼ K e(1=2)½(umu )=su
2
where the quantities, mu and su are defined by mu ¼
ns2Q m^ Y , ns2Q þ s2W
sW sQ su ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ns2Q þ s2W
and K is another function independent of u. The marginal density f(y) can be given as ð1 pffiffiffiffiffiffi pffiffiffiffiffiffi 2 1 pffiffiffiffiffiffi f (y) ¼ K 2p su e(1=2)½(umu )=su du ¼ K 2p su 2p su 1 Substituting for f(y,u) and f(y) in the posterior density f(ujy), we obtain 2
f (u j y) ¼
2 f (y,u) K e(1=2)½(umu )=su 1 pffiffiffiffiffiffi ¼ ¼ pffiffiffiffiffiffi e(1=2)½(umu )=su f (y) 2p su K 2p su
388
Estimation Theory
Hence, the posterior density is also Gaussian-distributed with mean mu and variance s2u , and the conditional expectation of Q given the observed values of y using Bayesian estimation techniques is E½Q j y ¼ mu ¼
ns2Q m^ Y ns2Q þ s2W
Note that using classical estimation techniques where there is no knowledge of the parameters of Q, the conditional expectation of Q given the observed values of y is ^ ¼ m^ E½Q j y ¼ Q Y obtained by substituting s2Q ¼ 1 in the Bayesian estimate mu. Example 18.6.2 A machine of uncertain quality manufactures microchips. In the absence of any information, we shall assume a priori that the bad microchips are uniformly distributed in the interval [0,1]. We take a sample of 10 microchips and find that 3 are bad. We want to estimate the probability of the bad microchips manufactured. 3 The prior probability of bad microchips from classical method ¼ 10 ¼ 0:3: We find the posterior probability from Eq. (18.6.2). Here n ¼ 10 and k ¼ 3; hence the posterior probability is (3 þ 1)/(10 þ 2) ¼ 0.3333, indicating that the proportion of bad chips is worse than what we might obtain by the classical method. On the other hand, if the 2 number of bad chips out of 10 is only 2, then the prior will be 10 ¼ 0:2. The Bayesian probability will yield (2 þ 1)/(10 þ 2) ¼ 0.25. In this case also, the proportion of bad chips is worse than that obtained from the classical method.
Maximum A Posteriori Probability (MAP) Estimation
We will now discuss the important problem of maximizing the posterior probability to arrive at a decision. A communication receiver with additive noise is given by xi ¼ si þ wi ,
i ¼ 1, . . . , n, or
x¼sþw
(18:6:13)
where fxig is the observation process defined by the n-vector x, fsig is the signal process defined by the n-vector s, and fwig is a zero mean random noise defined by the n-vector w. Without loss in generality, we will assume that the signal vector is either all 0 or 1. We observe x and decide on one of the following two hypotheses: H0 : xi ¼ wi , i ¼ 1, . . . , n,
or x ¼ w:
H1 : xi ¼ si þ wi , i ¼ 1, . . . , n or
0 transmitted
x ¼ s þ w:
1 transmited
where H0 is the null hypothesis and H1 is the alternate hypothesis. The prior probabilities of these hypotheses are P(H0) and P(H1). The probability density functions of observing x conditioned on H0 and H1 are fX(x j H0) and fX (x j H1). The posterior probabilities of H0 and H1 after observing x are P(H0 j x) and P(H1 j x). The decision will be based on maximizing the posterior probabilities. We choose the following decision criterion: P(H0 j x) . P(H1 j x):
choose H0
P(H0 j x) , P(H1 j x):
choose H1
18.6. Bayesian Estimation
389
FIGURE 18.6.1
Or more succinctly, the decision criterion is H P(H1 jx) 1 _ P(H0 j x) H0
1
(18:6:14)
If fHi, i ¼ 1,2g are the two hypotheses, then, from Bayes’ rule, we obtain P(Hi j x) ¼
P(Hi , x) fX (x j Hi )P(Hi ) ¼ , fx (x) fX (x)
i ¼ 0,1
(18:6:15)
Substituting Eq. (18.6.15) in Eq. (18.6.14), the decision rule becomes fX (x j H1 )P(H1 ) H1 fX (x) _ fX (x j H0 )P(H0 ) H0 fX (x)
1
or
H fX (x j H1 )P(H1 ) 1 _ fX (x j H0 )P(H0 ) H0
1
(18:6:16)
Transposing the prior probabilities to the right of the inequality, we have L(x) ¼
H fX (x j H1 ) 1 P(H0 ) ¼g _ fX (x j H0 ) P(H1 ) H0
(18:6:17)
where L(x) is defined as the likelihood function or likelihood ratio and g is the threshold value or the critical point. Since L(x) is the ratio of n-fold probability densities, we prefer to take logarithm of L(x). The inequality of Eq. (18.6.17) will be preserved since the logarithm is a monotone function. Thus the log likelihood ratio ln½L(x) is given by H1 P(H0 ) ln½L(x) _ ln ¼ ln g P(H1 ) H0
(18:6:18)
The inequality given by this equation maximizes the posterior probability. We can now represent the decision receiver as shown in Fig. 18.6.1.
Minimization of Average Probability of Error
In the previous section the posterior probability was maximized to arrive at a decision rule. We can approach this problem through minimizing the average probability of error. The probabilities of missed alarm a and false alarm b are given by a ¼ P(accept H1 j H0 true) ¼ P(H1 j H0 ) b ¼ P(accept H0 j H1 true) ¼ P(H0 j H1 ) a, b, and the acceptance and rejection regions are as shown in Fig. 18.6.2.
(18:6:19)
390
Estimation Theory
FIGURE 18.6.2
The probability of average error Pe is given by Pe ¼ P(average error) ¼ P(H1 j H0 )P(H0 ) þ P(H0 j H1 )P(H1 ) ð ð ¼ P(H0 ) fX (x j H0 )dx þ P(H1 ) fX (x j H1 )dx R1
(18:6:20)
R0
If R is the entire region of integration, then R ¼ R0 þ R1 and ð ð fX (x j H0 )dx ¼ fX (x j H1 )dx ¼ 1 R
(18:6:21)
R
Using Eq. (18.6.21), we can convert Eq. (18.6.20) into integration over the region R0 ð ð Pe ¼ P(H0 ) fX (x j H0 )dx fX (x j H0 )dx R
R0
ð
þ P(H1 )
fX (x j H1 )dx R0
ð ¼ P(H0 ) þ
½P(H1 )fX (x j H1 ) P(H0 )fX (x j H0 )dx
(18:6:22)
R0
where P(H0) is always positive, and minimizing Pe depends on the function g(x) ¼ P(H1 )fX (x j H1 ) P(H0 )fX (x j H0 ) under the integral sign. A plot of g(x) is shown in Fig. 18.6.3. From the figure, we obtain P(H1 )fX (x j H1 ) . P(H0 )fX (x j H0 ):
g(x) is positive ) x [ R1
P(H1 )fX (x j H1 ) , P(H0 )fX (x j H0 ):
g(x) is negative ) x [ R0
(18:6:23)
Hence we have the decision criterion H fX (x j H1 )P(H1 ) 1 _ fX (x j H0 )P(H0 ) H0
1
or
H fX (x j H1 ) 1 P(H0 ) _ fX (x j H0 ) P(H1 ) H0
which is exactly the same as Eq. (18.6.17) derived for the MAP criterion.
(18:6:24)
18.6. Bayesian Estimation
391
FIGURE 18.6.3 Maximum-Likelihood Estimation
We have already formulated the basic ML criterion in Eq. (18.1.7) as the maximization of the loglikelihood function ln L(x,u). We will discus this important estimation criterion more formally. X is a continuous random variable with density function fX (x;u), where u is a k-vector given by u ¼ [u1, u2, . . . , uk]T are parameters to be estimated by observing n (n . k) independent observations x1, x2, . . . , xn of the random variable X. The likelihood function L(x,u) is defined by L(x,u) ¼
n Y
fX (xi ,u)
i¼1
and the loglikelihood function, by ln½L(x,u) ¼
n X
ln ½fX (xi ,u)
(18:6:25)
i¼1
Maximizing the loglikelihood function ln[L(x,u)] with respect to fui, i ¼ 1, . . . , kg and setting them equal to zero yields k equations given by @ {ln½L(x,u1 , . . . , uk )} ¼ 0, @ui
i ¼ 1, . . . , k
(18:6:26)
The estimates of the k parameters (u1,u2, . . . , uk) are obtained by solving the k equations in Eq. (18.6.26). We will briefly discuss the properties of ML estimators: 1. They provide a consistent approach to parameter estimation problems. 2. They can be used to generate hypothesis tests for parameters. 3. The likelihood equations need to be specifically worked out for any given distribution and estimation problems. 4. The numerical estimation problem is usually nontrivial. Simple solutions are possible in only a few cases. 5. Maximum-likelihood estimates can be heavily biased for small samples. Example 18.6.3 In an election we want to estimate the proportion p of voters who will vote Democratic. We will assign 1 if a person votes Democratic and 0 if he
392
Estimation Theory
does not vote Democratic. The probability f (x;p) for each person is a Bernoulli distribution with f (x;p) ¼ px (1 p)1x ,
x ¼ 0,1;
0p1
The value of p has to be estimated using the ML rule based on an opinion poll of n voters chosen at random. Thus, there will be n sample values x1, x2, . . . , xn, each with probability f (xi ;p) ¼ pxi (1 p)1xi ,
xi ¼ 0,1; 0 p 1; i ¼ 1, . . . , n
The likelihood function and the loglikelihood functions are given by n Pn Pn Y pxi (1 p)1xi ¼ p i¼1 xi (1 p)n i¼1 xi L(x;p) ¼ i¼1
ln½L(x;p) ¼
n X
xi ln(p) þ n
i¼1
n X
! xi ln(1 p)
i¼1
Differentiating ln[L(x;p)] with respect to p, we have
P Pn n ni¼1 xi @ i¼1 xi ln½L(x;p) ¼ ¼0 @p 1p p We obtain the estimate p^ by solving for p in this equation: p^ ¼
n 1X xi ¼ m^ X n i¼1
where p^ is an unbiased estimator because E½^p ¼ E½m^ X ¼ p 1 þ (1 p) 0 ¼ p. Example 18.6.4 Telephone calls coming into an exchange are Poisson-distributed with parameter l and are given by fX (x;l) ¼ el
lx , x!
x ¼ 0,1, . . .
where l is to be estimated using the ML rule by observing a random sample x1, x2, . . . , xn, of n telephone calls, each with probability fX (xi ;l) ¼ el
lxi , xi !
i ¼ 1,2, . . . , n
The likelihood function and the loglikelihood functions are given by Pn n xi Y enl l i¼1 xi l l L(x;l) ¼ ¼ Qn e xi ! i¼1 xi ! i¼1 ln½L(x;l) ¼ nl þ
n X i¼1
xi ln l
n X
ln (xi !)
i¼1
Differentiating ln[L(x; l)] with respect to l, we have Pn @ xi ln½L(x; l) ¼ n þ i¼1 ¼ 0 @l l
18.6. Bayesian Estimation
393
We obtain the estimate l^ by solving for l in the preceding equation: 1 l^ ¼ n
n X
xi ¼ m^ X
i¼1
^ ¼ E½m^ X ¼ l. where l^ is an unbiased estimator because E½l Example 18.6.5 A sample of observations fxi, i ¼ 1, . . . , ng drawn from a popu2 lation governed by a form of Weibull density is given by fX (x) ¼ 2lxelx u(x) with parameter l. We want to estimate lusing the ML criterion. The likelihood function is given by ½L(x;l) ¼
n Y
fX (xi ) ¼ (2l)n
i¼1
n Y
xi el
Pn
x2 i¼1 i
,
xi . 0
i¼1
and the loglikelihood function by ln½L(x;l) ¼ n ln(2) þ n ln(l) þ
n X
ln(xi ) l
i¼1
n X
x2i ,
xi . 0
i¼1
Differentiating ln[L(x;l)] with respect to l, we obtain n @ n X ln½L(x; l) ¼ x2 ¼ 0 @l l i¼1 i
Solving for l in this equation, we obtain the estimate l^ as n l^ ¼ Pn
2 i¼1 xi
¼
1 b m2
where
m2 ¼ E½X 2
^ 2 ¼ m2 , but E½1=b We have E½X 2 ¼ m2 ¼ 1=l and E½m m2 = 1=E½b m2 ¼ 1=m2 ¼ l, and hence l^ is a biased estimator. Example 18.6.6 A random sample of n is drawn from a Gaussian-distributed population of mean mX and variance s2X. We want to estimate the mean and the variance using the ML rule. The likelihood function and the loglikelihood function are shown below: n Y
2 1 2 pffiffiffiffiffiffi e(1=2sX )(xi mX ) 2p s X i¼1 Pn 2 2 1 ¼ e(1=2sX ) i¼1 (xi mX ) n=2 n (2p) sX
L(x;mX , s2X ) ¼
n n n 1 X ln½L(x;mX ,s2X ) ¼ ln(2p) ln(s2X ) 2 (xi mx )2 2 2 2sX i¼1
394
Estimation Theory
Differentiating with respect to mX and s2X , and setting them equal to zero, we have n @ 1 X ln½L(x;mX , s2X ) ¼ 2 (xi mX ) ¼ 0 @mX sX i¼1 n @ n 1 X 2 ln½L(x;m , s ) ¼ þ (xi mX )2 ¼ 0 X X @s2X 2s2X 2s4X i¼1
Solving these equations, we find the estimators as follows: m^ X ¼
n 1X xi n i¼1
and
s^ 2X ¼
n 1X (xi mX )2 n i¼1
In these estimators, while the estimator for the mean m^ X is unbiased, the estimator for the variance s^ 2X is biased because E½s^ 2X ¼ ½(n 1)=ns2X .
ML Rule in Hypothesis Testing
In the previous section we saw that the MAP rule minimizes the average probability of wrong decisions using the prior probabilities PfHi,i ¼ 0,1g of the two hypotheses. In many cases the priors may not be exactly known. In such a case, we choose whichever conditional probability P(x j H0) or P(x j H1) is greater, thereby making the assumption that both hypotheses H0 and H1 are equiprobable. We will show later in an example that when the priors are not known precisely, the decision errors will be more than that when the priors are equiprobable. This decision rule where the priors are assumed equiprobable is called the maximum-likelihood (ML) rule for testing hypotheses and is stated as follows: P(x j H0 ) . P(x j H1 ): P(x j H0 ) , P(x j H1 ):
accept H0 accept H1
(18:6:27)
In terms of density functions Eq. (18.6.27) can be given more succinctly as H fx (x j H1 ) 1 _ fx (xjH0 ) H0
1
(18:6:28)
In many applications the ML rule is widely used, especially when the average probability of error Pe is low. Further, the missed-alarm probability is much more important than the false-alarm probability. For example, missing the diagnosis of cancer has more serious consequences than does diagnosing cancer when there is none. The MAP rule is prone to yielding higher miss probabilities than is the ML rule as we will see in the following example. Example 18.6.7 The output x of a system consists of an input s ¼ 0 or 1 and a zero mean additive noise w with variance s2W ¼ 14. The prior probability of sending s ¼ 0 is P(s ¼ 0) ¼ 3 1 4 and that of s ¼ 1 is P(s ¼ 1) ¼ 4. We have to find the following using both the MAP and the ML rules: (1) the decision threshold g, (2) the average probability of error Pe (x), and (3) the probabilities of missed alarm and false alarm. We can formulate the hypotheses as
395
18.6. Bayesian Estimation
FIGURE 18.6.4
follows: 3 4 1 H1 : {1 transmitted} with P(H1 ) ¼ 4
H0 : {0 transmitted} with P(H0 ) ¼
The conditional probabilities of x under the two hypotheses are 2 2 fX (x j H0 ) ¼ pffiffiffiffiffiffi e(1=2)½4x 2p
2 2 fX (x j H1 ) ¼ pffiffiffiffiffiffi e(1=2)½4(x1) 2p
and
These probability density functions are shown in Fig. 18.6.4. The likelihood ratio L(x) is given by 2
fX (x j H1 ) e(1=2)½4(x1) 2 2 ¼ (1=2)½4x2 ¼ e2½x 2xþ1x ¼ e2(2x1) L(x) ¼ fX (x j H0 ) e MAP Criterion. The decision rule from the loglikelihood function of Eq. (18.6.18) is ln½e
2(2x1)
H1 3=4 _ ln 1=4 H0
or
H1 1 ln 3 x_ þ 2 4 H0
or
H1 x _ 0:7747 H0
Thus, the critical point for MAP is g ¼ 0.7747 as shown in Fig. 18.6.4. The probability of average error Pe(x) for any value of x from Eq. (18.6.22) is given by ðx PeMAP (x) ¼ P(H0 ) þ
½P(H1 )fX (j j H1 ) P(H0 )fX (j j H0 )dj 1
ð 3 2 x 1 2(j1)2 3 2j2 þ pffiffiffiffiffiffi e e dj 4 4 2p 1 4 pffiffiffi pffiffiffiffiffiffi 1 1 ¼ þ erf½ 2(x 1) 3 erf½ 2p ; 2 8
¼
2 erf(x) ¼ pffiffiffiffi p
ðx 0
2
ej dj
396
Estimation Theory
The minimum error is obtained when x ¼ g ¼ 0.7747 and Pe(min) ¼ 0.127. The probability of missed alarm is obtained from the following integral ðg PMAMAP ¼
2 2 pffiffiffiffiffiffi e2(x1) dx ¼ 0:3261 2p 1
and the probability of false alarm from ð1 PFAMAP ¼ g
2 2 pffiffiffiffiffiffi e2x dx ¼ 0:0607 2p
These probabilities are also shown in Fig. 18.6.4. The function gMAP (x) under the integral, that governs the extremum for PeMAP (x) is given by 2 2 1 3 2 gMAP (x) ¼ pffiffiffiffiffiffi e2(x1) e2x 4 2p 4 ML Criterion. We will now obtain the same quantities using the ML criterion. Here the decision criterion becomes H1 1 x+ 2 H0 and the critical point for ML is 0.5. Hence the probability of error PeML (x) is given by ð 1 1 x ½ fX (j j H1 ) fX (j j H0 ) dj PeML (x) ¼ þ 2 2 1 ð 2 2 1 1 x ¼ þ pffiffiffiffiffiffi ½e2(j1) e2j dj 2 2p 1 h hpffiffiffiffiffiii pffiffiffi 1 1 ¼ þ erf 2(x 1) erf 2x 2 4 The minimum error is obtained when x ¼ g ¼ 0.5 and Pe(min) ¼ 0.159. The probability of missed alarm is obtained from the following integral ð 0:5 2 2 pffiffiffiffiffiffi e2(x1) dx ¼ 0:1587 PMAML ¼ 2p 1 and the probability of false alarm from, ð1 2 2 pffiffiffiffiffiffi e2x dx ¼ 0:1587 PFAML ¼ 2p 0:5 Note that both probabilities are the same for the maximum-likelihood criterion. The function gML(x) under the integral that governs the extremum for PeML (x) is given by i 2 2 h 2 gML (x) ¼ pffiffiffiffiffiffi e2(x1) e2x 2p The average probabilities of error PeMAP (x) and PeML (x) are shown in Fig. 18.6.5. Table 18.6.1 compares the two decision criteria.
18.6. Bayesian Estimation
397
FIGURE 18.6.5
TABLE 18.6.1 Category P(H0)—probability of null hypothesis P(H1)—probability of alternate hypothesis g—critical point Pe(min)—minimum probability of average error PMA—probability of missed alarm PFA—probability of false alarm
MAP Rule
ML Rule
0.75 0.25 0.7747 0.127 0.3261 0.0607
0.5 0.5 0.5 0.159 0.1587 0.1587
Comparing the two methods, we see that the minimum probability of average error is smaller for the MAP than for the ML. However, the probability of the more important missed alarm is higher for MAP than for ML. Thus, in most communication problems the ML criterion is the preferred method of estimation. The functions gMAP (x) and gML (x) for both MAP and ML criteria are shown in Fig. 18.6.6.
FIGURE 18.6.6
398
Estimation Theory
In Fig. 18.6.6 the function gML(x) corresponding to the ML rule has an odd symmetry, and hence it integrates to zero. The critical point is 12, as seen before. Example 18.6.8 (Discrete Probability) An extrusion company has contracted to supply plastic shopping bags to supermarkets. Ordinarily, the probability of tearing in this group A0 of bags is 0.01. Because of a production malfunction, a second group A1 of bags have a higher probability of 0.05 of tearing. The prior probabilities of choosing A0 or A1 are 0.7 and 0.3 respectively. To find out which group of bags came from A1, the company tests a sample of n bags sequentially until it comes across a first failure. From this information it must decide whether the bags came from group A0 or A1. Using both the MAP and the ML criteria, we have to find (1) the number n of bags required to be tested, (2) the probability of average error Pe(n), and (3) probabilities of missed and false alarms PMA and PFA. We will first approach the problem in generality and later substitute numbers to obtain results. Let q0 be the tearing probability in group A0 and q1 be the tearing probability in group A1. The two hypotheses can be defined by H0 : H1 :
bag came from group A0 bag came from group A1
(18:6:29)
The conditional probabilities of the waiting times upto the first tearing are geometrically distributed and are given by P(n j H0 ) ¼ q0 (1 q0 )n1 ;
P(n j H1 ) ¼ q1 (1 q1 )n1
(18:6:30)
MAP Criterion. According to the MAP criterion of Eq. (18.6.14), the decision rule is P(H1 j n) H 1 _1 P(H0 j n) H 0
or
P(n j H1 )P(H1 ) H 1 _1 P(n j H0 )P(H0 ) H 0
or
P(n j H1 )P(H1 ) H1 P(n) _1 P(n j H0 )P(H0 ) H 0 P(n)
or (18:6:31)
P(n j H1 ) H 1 P(H0 ) _ P(n j H0 ) H 0 P(H1 )
Substituting the conditional probabilities from Eq. (18.6.30) into Eq. (18.6.31), we have q1 1 q1 n1 H 1 P(H0 ) _ q0 1 q0 H 0 P(H1 )
(18:6:32)
Taking logarithms on both sides of Eq. (18.6.32) and simplifying, we obtain q0 P(H0 ) H 1 ln q1 P(H1 ) (n 1) _ 1 q1 H0 ln 1 q0
or
H1 n _ nMAP H0
q0 P(H0 ) ln q1 P(H1 ) ¼1þ 1 q1 ln 1 q0
(18:6:33)
18.6. Bayesian Estimation
399
where nMAP is the critical value of n. Thus we have the decision criterion for n: If n . nMAP : accept hypothesis H1 If n , nMAP : accept hypothesis H0
(18:6:34)
This MAP decision criterion gives us the minimum number n that are needed to be tested sequentially. Probabilities of Missed and False Alarms and Average Error. We will now calculate the probabilities of missed and false alarms as functions of n. The probability of missed alarm PMA(n) is given by PMA (n) ¼ P(N . n j H1 ) ¼
1 X
q1 (1 q1 )k1
k¼nþ1
¼1
n X
q1 (1 q1 )k1 ¼ 1 q1
k¼1
1 (1 q1 )n ¼ (1 q1 )n 1 (1 q1 )
(18:6:35)
where we have used the result for the finite summation of a geometric series. Similarly, the probability of false alarm PFA (n) is given by PFA (n) ¼ P(N n j H0 ) ¼
n X
q0 (1 q0 )k1
k¼1 n
¼ q0
1 (1 q0 ) ¼ 1 (1 q0 )n 1 (1 q0 )
(18:6:36)
Finally, the probability of average error PeMAP(n) is given by PeMAP (n) ¼ (1 q1 )n P(H1 ) þ ½1 (1 q0 )n P(H0 )
(18:6:37)
We obtain the various probabilities by substituting n ¼ nMAP in Eqs. (18.6.35) – (18.6.37). ML Criterion. For the ML criterion, the hypotheses H0 and H1 are equiprobable, and substituting P(H0) ¼ P(H1) in Eq. (18.6.36), we obtain q0 ln H1 q n _ nML ¼ 1 þ 1 (18:6:38) 1 q1 H0 ln 1 q0 with the decision criterion similar to that of Eq. (18.6.37). Equations for the probabilities of missed and false alarms for the ML criterion as functions of n are exactly the same as those of the MAP criterion except that we substitute n ¼ nML. The probability of average error for the ML criterion can be obtained from Eq. (18.6.37) by substituting P(H0) ¼ P(H1) ¼ 12, and the resulting equation is PeML (n) ¼
1 1 þ (1 q1 )n (1 q0 )n 2 2
(18:6:39)
Numerical Solutions. The conditional probability functions P(n j H0) ¼ 0.01 (1 2 0.01)n and P(n j H1) ¼ 0.05 (1 2 0.05)n are shown in Fig. 18.6.7.
400
Estimation Theory
FIGURE 18.6.7
Substituting for the MAP criterion, q0 ¼ 0.01, q1 ¼ 0.05, P(H0) ¼ 0.7, and P(H1) ¼ 0.3 in Eq. (18.6.39), we obtain the critical value nMAP: 0:01 0:7 ln 0:05 0:3 ¼ 19:4793 nMAP ¼ 1 þ 1 0:05 ln 1 0:01 Or, using the MAP criterion, the minimum number of bags to test sequentially is n ¼ 20. The missed-alarm probability is obtained from Eq. (18.6.38): PMA (n) ¼ (1 q1 )n ¼ 0:95n , and the a error is 0.9520 ¼ 0.3585. The false-alarm probability is obtained from Eq. (18.6.39): PFA (n) ¼ 1 (1 q0 )n ¼ 1 0:99n , and the b error is 1 2 0.9920 ¼ 0.1821. The probability of average error PeMAP(n) is obtained from Eq. (18.6.37): PeMAP (n) ¼ 0:95n 0:3 þ (1 0:99n ) 0:7 and PeMAP(min) ¼ PeMAP(20) ¼ 0.235. For the ML criterion, we substitute q0 ¼ 0.01, q1 ¼ 0.05, P(H0) ¼ 0.5, and P(H1) ¼ 0.5 in Eq. (18.6.38) and obtain the critical value nML: 0:01 ln 0:05 ¼ 40:0233 nML ¼ 1 þ 1 0:05 ln 1 0:01 Or, using the ML criterion, the minimum number of bags to test sequentially is n ¼ 41. The missed-alarm probability is obtained from Eq. (18.6.38): PMA (n) ¼ 1 (1 q1 )n ¼ 0:95n , and the a error is 0.9541 ¼ 0.1221. The false-alarm probability is obtained from Eq. (18.6.39): PFA (n) ¼ 1 (1 q0 )n ¼ 1 0:99n , and the b error is 1 2 0.9941 ¼ 0.3377. The probability of average error PeMAP(n) is obtained from Eq. (18.6.39): PeML (n) ¼ 12 þ 12 ½0:95n þ 0:99n and PeML(min) ¼ PeML(41) ¼ 0.2299. Probabilities PeMAP (n) and PeML (n) are shown in Fig. 18.6.8 along with the minimum number of bags needed for both the MAP and ML criteria. The results indicate that the number of bags needed using the MAP is half the number needed for ML. The more important missed-alarm probability for the ML criterion
18.6. Bayesian Estimation
401
FIGURE 18.6.8
is considerably lower than that of the MAP criterion. The ML criterion is more often used in communication problems. The MAP criterion does find applications in medical imaging problems such as single-photon emission computed tomography (SPECT).
Hypothesis Testing with Added Cost for Errors
In the previous sections we never considered the costs associated with type I and type II errors. We have already mentioned that the missed-alarm or type I error is more serious than the false-alarm or type II error. We will now form a decision rule that takes into account the costs associated with the errors. We shall postulate the true hypotheses by H0 and H1 and the define the accepted hypotheses by A0 and A1. There will be four pairs of decisions, two of which, fA0,H0g and fA1,H1g, are correct decisions and the other two of which, fA0,H1g (missed alarm) and fA1,H0g (false alarm) are incorrect decisions. We will associate a cost Cij for each pair fAi,Hj,i,j ¼ 1,2g of the four decisions. The average cost of the decisions C for the MAP criterion is C ¼ C00 P(A0 , H0 ) þ C11 P(A1 ,H1 ) þ C01 P(A0 ,H1 ) þ C10 P(A1 ,H0 )
(18:6:40)
where P(Ai,Hj) i,j ¼ 1,2 represents the joint probability of accepting Ai, given that Hj is true. We have to form a decision rule that minimizes the average cost. Expressing Eq. (18.6.40) in terms of conditional probabilities, we have C ¼ C00 P(A0 j H0 )P(H0 ) þ C01 P(A0 j H1 )P(H1 ) þ C11 P(A1 j H1 )P(H1 ) þ C10 P(A1 j H0 )P(H0 )
(18:6:41)
where C01 is the cost of the missed alarm and C10 is the cost of the false alarm. Substituting conditional density functions for the conditional probabilities, there results ð ð C MAP ¼ C00 P(H0 ) fX (x j H0 )dx þ C01 P(H1 ) fX (x j H1 )dx R0
R0
ð
ð
þ C11 P(H1 )
fX (x j H1 )dx þ C10 P(H0 ) R1
fX (x j H0 )dx R1
(18:6:42)
402
Estimation Theory
We can transform the integrals in Eq. (18.6.42) to be over the same region R0: ð ð C MAP ¼ C00 P(H0 ) fX (x j H0 )dx þ C01 P(H1 ) fX (x j H1 )dx R0
R0
ð ð fX (x j H1 )dx þ C10 P(H0 ) 1 fX (x j H0 )dx (18:6:43) þ C11 P(H1 ) 1 R0
R0
Combining like terms in Eq. (18.6.43), we obtain ð CMAP ¼ C11 P(H1 ) þ C10 P(H0 ) þ ½P(H1 )(C01 C11 )fX (x j H1 ) R0
P(H0 )(C10 C00 )fX (x j H0 )dx
(18:6:44)
At this point it is natural to assume that the cost for an incorrect decision exceeds the cost for a correct decision, or C10 . C00 and C01 . C11. With this assumption, the terms P(H1 )ðC01 C11 ÞfX fX (x j H1 ) and P(H0 )ðC10 C00 ÞfX (x j H0 ) are both positive. Hence, if the integrand I ¼ P(H1 )(C01 C11 ÞfX (x j H1 ) PðH0 Þ(C10 C00 )fX (x j H0 ) is negative, then we can conclude that x [ R0 and choose H0. Thus the decision rule for the MAP criterion is fX (x j H1 )P(H1 )(C01 C11 ) H 1 _1 fX (x j H0 )P(H0 )(C10 C00 ) H 0 or L(x) ¼
fX (x j H1 ) H 1 P(H0 )(C10 C11 ) _ fX (x j H0 ) H 0 P(H1 )(C01 C11 )
(18:6:45)
and the decision rule for the ML criterion can be obtained by substituting P(H0) ¼ P(H1) in Eq. (18.6.45): L(x) ¼
fX (x j H1 ) H 1 C10 C00 _ fX (x j H0 ) H 0 C01 C11
(18:6:46)
In Eqs. (18.6.45) and (18.6.46), C10 is the cost associated with a false alarm and C01 is for a missed alarm. We weigh the missed alarm more heavily than the false alarm with the result C01 . C10. Hence the ratio (C10 C00 =(C01 C11 ) will always be lesser than 1 if the costs associated with right decisions are the same. Example 18.6.9 We will reexamine Example 18.6.7, assigning cost functions to wrong decisions. We will assume that the costs for the right decisions are the same, C00 ¼ C11 ¼ 1, and the cost for a missed alarm to be C01 ¼ 4C00 and the cost for a false alarm, C10 ¼ 2C00. The prior probabilities are P(H0) ¼ 34 and P(H1) ¼ 14. The conditional probabilities of x from Example 18.6.7 under the two hypotheses are 2 2 fX (x j H0 ) ¼ pffiffiffiffiffiffi e(1=2)½4x 2p
2 2 and fX (x j H1 ) ¼ pffiffiffiffiffiffi e(1=2)½4(x1) 2p
The likelihood ratio L(x) is L(x) ¼
fX (x j H1 ) ¼ e2(2x1) fX (x j H0 )
18.6. Bayesian Estimation
403
From Eq. (18.6.45), the MAP criterion for minimization of cost functions is H 1 P(H0 )(C10 C00 ) 3C00 (2 1) ¼ ¼1 L(x) ¼ e2(2x1) _ C00 (4 1) H 0 P(H1 )(C01 C11 Þ The critical point from the loglikelihood function can be obtained as follows: H1 2(2x 1) _ 0 H0
or
H1 1 x_ H0 2
or gMAP ¼
1 2
The average cost function C MAP (x) obtained from Eq. (18.6.44) is given by ð 1 3 2 x CMAP (x) ¼ C00 þ 2 C00 þ pffiffiffiffiffiffi 4 4 2p 1 2 2 1 3 (4 1)e2(j1) (2 1)e2j dx 4 4 n h i pffiffiffi pffiffiffi o 7 3 ¼ C00 þ erf 2(x 1) erf 2x 4 8 Assuming that C00 ¼ 1, the minimum cost function C MAP(min) ¼ CMAP (0:5) ¼ 1:238. The probability of missed alarm is obtained from the following integral ð gMAP 2 2(x1)2 1 1 pffiffiffiffiffiffi e dx ¼ 1 erf pffiffiffi ¼ 0:1587 PMAMAP ¼ 2 2p 2 1 and the probability of false alarm, from ð1 2 2x2 1 1 pffiffiffiffiffiffi e dx ¼ 1 erf pffiffiffi ¼ 0:1587 PFAMAP ¼ 2 2p 2 gMAP Similarly, the ML criterion for minimization of cost functions can be obtained by substituting P(H0) ¼ P(H1) ¼ 12 in Eq. (18.6.44). The result is H 1 C10 C00 C00 (2 1) 1 ¼ ¼ L(x) ¼ e2(2x1) _ H 0 C01 C11 C00 (4 1) 3 The critical point from the loglikelihood function can be obtained as follows: H 1 1 H 1 1 ln (3) 2(2x 1) _ ln or gML ¼ 0:2253 or _ 3 4 H0 H0 2 The average cost function C ML (x) is ðx 2 2 C00 2 (1 þ 2) þ pffiffiffiffiffiffi C00 (4 1)e2(j1) (2 1)e2j dx 2 2 2p 1 h i 1 h pffiffiffi i pffiffiffi 3 ¼ C00 2 þ erf 2(x 1) erf 2x 4 4
CML (x) ¼
Assuming that C00 ¼ 1, the minimum cost function C ML(min) ¼ CML (0:2253) ¼ 1:254. The probability of missed alarm is obtained from the following integral ð gML 2 2 pffiffiffiffiffiffi e2(x1) dx ¼ 0:0607 PMAML ¼ 2p 1
404
Estimation Theory
FIGURE 18.6.9
and the probability of false alarm, from ð1 PFAML ¼ gML
2 2 pffiffiffiffiffiffi e2x dx ¼ 0:3261 2p
The probability density functions, the critical point gML, and the error regions for the ML criterion are shown in Fig. 18.6.9. The cost functions C MAP (x) and C ML (x) along with their critical points gMAP and gML are shown in Fig. 18.6.10. The function gMAP(x) under the integral that governs the extremum for PeMAP(x) is 2 2 1 gMAP (x) ¼ pffiffiffiffiffiffi e2(j1) 3e2j 2 2p
FIGURE 18.6.10
18.6. Bayesian Estimation
FIGURE 18.6.11
TABLE 18.6.2 Category P(H0)—probability of null hypothesis P(H1)—probability of alternate hypothesis C00 ¼ C11—cost of correct decisions C01—cost of missed alarm C10—cost of false alarm g—critical point C min —minimum cost of average error PMA—probability of missed alarm PFA—probability of false alarm
MAP Rule
ML Rule
0.75 0.25 1 4 2 0.5 1.238 0.1587 0.1587
0.5 0.5 1 4 2 0.2253 1.254 0.0607 0.3261
The function gML(x) under the integral, which governs the extremum for PeML(x), is 2 2 1 gML (x) ¼ pffiffiffiffiffiffi 3e2(j1) e2j 2 2p These two functions gMAP(x) and gML(x) are shown in Fig. 18.6.11. Table 18.6.2 compares the two decision criteria.
405
CHAPTER
19
Random Processes
B 19.1
BASIC DEFINITIONS
In the earlier sections a random variable X was defined as a function that maps every outcome ji of points in the sample space S to a number X(ji) on the real line R. A random process X(t) is a mapping that assigns a time function X(t,ji) to every outcome ji of points in the sample space S. Alternate names for random processes are stochastic processes and time series. More formally, a random process is a time function assigned for every outcome j [ S according to some rule X(t,j), t [ T, j [ S, where T is an index set of time. As in the case of a random variable, we suppress j and define a random process by X(t). If the index set T is countably infinite, the random process is called a discrete-time process and is denoted by Xn. Referring to Fig. 19.1.1, a random process has the following interpretations: 1. X(j,t1) is random variable for a fixed time t1. 2. X(ji,t) is a sample realization for any point ji in the sample space S. 3. X(ji,t2) is a number. 4. X(j,t) is a collection or ensemble of realizations and is called a random process. An important point to emphasize is that a random process is a finite or an infinite ensemble of time functions and is not a single time function. Example 19.1.1 A fair coin is tossed. If heads come up, a sine wave x1(t) ¼ sin(5pt) is sent. If tails come up, then a ramp x2(t) ¼ t is sent. The resulting random process X(t) is an ensemble of two realizations, a sine wave and a ramp, and is shown in Fig. 19.1.2. The sample space S is discrete. Example 19.1.2 In this example a sine wave is in the form X(t) ¼ A sin(vt þ F), where F is a random variable uniformly distributed in the interval (0, 2p). Here the sample space is continuous, and the sequence of sine functions is shown in Fig. 19.1.3.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
406
19.1
FIGURE 19.1.1
FIGURE 19.1.2
FIGURE 19.1.3
Basic Definitions
407
408
Random Processes
Distribution and Density Functions. Since a random process is a random variable for any fixed time t, we can define a probability distribution and density functions as FX (x; t) ¼ P(j, t: X(j; t) x) for a fixed t
(19:1:1)
and fX (x; t) ¼
@ FX (x þ Dx; t) FX (x; t) FX (x; t) ¼ lim Dx!0 @x Dx ¼ lim P(x , X(t) x þ Dx) Dx!0
(19:1:2)
These are also called first-order distribution and density functions, and in general, they are functions of time. Means and Variances. Analogous to random variables, we can define the mean of a random process as ð1 mX (t) ¼ E ½X(t) ¼ xfX (x; t)dx (19:1:3) 1
and the variance as s2X (t) ¼ E ½X(t) mX (t)2 ¼ E ½X 2 (t) m2X (t) ð1 ½x mX (t)2 fX (x; t)dx ¼
(19:1:4)
1
where 2
ð1
x2 fX (x; t)dx
E½X (t) ¼
(19:1:5)
1
Since the density is a function of time, the means and variances of random processes are also functions of time.
Example 19.1.3 We shall now find the distribution and density functions along with the 7 mean and variance for the random process of Example 19.1.1 for times t ¼ 0, 12 , 10 : t ¼ 0,
x1 (0) ¼ 0,
x2 (0) ¼ 0
At t ¼ 0 the mapping diagram from the sample space to the real line is shown in Fig. 19.1.4a along with the corresponding distribution and density functions. The mean value is given by mX (0) ¼ 0; 12 þ 0 12 ¼ 0. The variance is given by 2 sX (0) ¼ (0 0)2 12 þ (0 0)2 12 ¼ 0: 1 1 1 1 t ¼ , x1 ¼ 1, x2 ¼ 2 2 2 2 At t ¼ 12 the mapping diagram from the sample space to the real line is shown in Fig. 19.1.4b along with the corresponding distribution and density functions. The mean value is given by mX 12 ¼ 12 12 þ 1 12 ¼ 34 ¼ 0:75:
19.1
Basic Definitions
409
FIGURE 19.1.4
The variance is given by s2X t¼
1 2
7 , 10
2 2 1 34 12 þ 1 34 12 ¼ 16 ¼ 0:0625: 7 7 7 x1 ¼ 1, x2 ¼ 10 10 10
¼
1 2
7 At t ¼ 10 the mapping diagram from the sample space to the real line is shown in Fig. 19.1.4c along with the corresponding distribution and density functions.
7 3 The mean value is given by mX 10 ¼ 0:15 ¼ 7 1 1 1 ¼ 20 7 10 322 1 2 2 7 3 21 The variance is given by sX 10 ¼ 10 þ 20 2 þ 1 þ 20 2 ¼ 289 400 ¼ 0:7225: Example 19.1.4 A random process, given by X(t) ¼ A sin(vt), is shown in Fig. 19.1.5, where A is a random variable uniformly distributed in the interval (0,1].
410
Random Processes
FIGURE 19.1.5
The density and distribution functions of A are fA (a) ¼
2
1, 0 , a 1 0, otherwise
0, FA (a) ¼ 4 a, 1,
a0 0,a1 a.1
We have to find the distribution function FX (x; t). For any given t, x ¼ asin(vt) is an equation to a straight line with slope sin(vt), and hence we can use the results of Examples 12.2.1 and 12.2.2 to solve for FX (x; t). The cases of sin(vt) . 0 and sin(vt) , 0 are shown in Fig. 19.1.6. Case 1: sin(vt) . 0. There are no points of intersection on the a axis for x 0, and hence FX(x; t) ¼ 0. For 0 , x sin(vt) we solve x ¼ a sin(vt) and obtain a ¼ x=½sin(vt). The region Ia for which a sin(vt) x is given by Ia ¼ {0 , a
FIGURE 19.1.6
19.1
Basic Definitions
411
x=½sin(vt)} (Fig. 19.1.6). Thus x x x FX (x; t) ¼ FA FA (0) ¼ FA ¼ sin(vt) sin(vt) sin(vt) Finally for x . sin(vt), the region Ia for which asin(vt) x, is given by Ia ¼ f0 , a 1g and FX (x; t) ¼ 1. Thus, for sin(vt) . 0, we have 2 0; x0 6 x , 0 , x sin(vt) FX (x; t) ¼ 4 sin(vt) 1, x . sin(vt) Case 2: sin(vt) , 0. The region Ia for which x . 0 is given by Ia ¼ f0 , a 1g, and hence FX (x; t) ¼ 1. For 2jsin(vt)j , x 0, we solve x ¼ –ajsin(vt)j and obtain a ¼ ½x=( j sin(vt)j). The region Ia for which –a jsin(vt)j x is given by x ,a1 Ia ¼ j sin(vt)j (Fig. 19.1.6). Thus, FX (x; t) ¼ FA (1) FA
x x ¼1 j sin(vt)j j sin(vt)j
Finally, for x – jsin(vt)j, the region Ia for which –a jsin(vt)j x is given by Ia ¼ f1 , a 1g and FX (x; t) ¼ 0. Thus, for sin(vt) , 0, we have 2 0, x j sin(vt)j x 61 , j sin(vt)j , x 0 FX (x; t) ¼ 4 j sin(vt)j 1, x.0 Case 3: sin(vt) ¼ 0. The region Ia for which x . 0 is given by Ia ¼ f0 , a 1g and FX (x; t) ¼ 1. For x 0, Ia ¼ 1 and FX (x; t) ¼ 0. Thus, for sin(vt) ¼ 0, we have 0, x 0 FX (x; t) ¼ 1, x . 0 Example 19.1.5 We shall now find the distribution and density functions along with the mean and variance for the random process of Example 19.1.2 and see how this process differs from the previous ones. We are given that X(t) ¼ A sin(vt þ F), where A is a constant and fF(f) ¼ 1/2p in the interval (0, 2p) and we have to find fX (x; t). We will solve this problem by (1) finding the distribution FX (x; t) and differentiating it, and (2) by direct determination of the density function. 1. Determination of Distribution Function FX (x; t). The distribution function for F is given by FF (f) ¼ (f=2p), 0 , f 2p. The two solutions for x ¼ A sin(vt þ f) are obtained from the two equations: sin(vt þ f1 ) ¼
x A
and
sin(p vt f2 ) ¼
x A
412
Random Processes
Hence the solutions are given by x f1 ¼ sin1 vt A
and f2 ¼ p sin1
x A
and are shown in Fig. 19.1.7. For x –A, there are no points of intersection and hence FX (x; t) ¼ 0. For 2A , x A, the set of points along the f axis such that A sin(vt þ f) x is (0, f1] < (f2, 2p]. Hence FX (x; t) is given by FX (x; t) ¼ FF (f1 ) FF (0) þ FF (2p) FF (f2 ) h x io 1 n 1 x sin ¼ vt 0 þ 2p p sin1 vt 2p A A n o 1 x 1 x 1 2 sin1 ¼ þ p ¼ sin1 þ , A , x A 2p A p A 2 Finally, for x . A, the entire curve A sin(vt þ f) is below x, and FX(x;t) ¼ 1. 2. Determination of Density Function fX (x; t) (a) The two solutions to x ¼ A sin(vt þ f) have been found earlier. (b) The absolute derivatives j@x=@fjjf1 and j@x=@fjjf2 are given by
h i
@x
¼ A cos(vt þ f1 ) ¼ A cos vt þ sin1 x vt
@f
A f1 p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi h x i A2 x2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ A cos sin1 ¼ A2 x 2 ¼A A A and
h i
@x
¼ A cos(vt þ f2 ) ¼
A cos vt þ p sin1 x vt
@f
A f2 p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
h x i
A2 x2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
¼ A cos p sin1 ¼ A2 x 2
¼A A A
FIGURE 19.1.7
19.1
Basic Definitions
413
(c) With the two solutions for x, the density function fX (x; t) is given by Eq. (12.3.6): 1 1 1 1 þ fX (x; t) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , A , x A 2 2 A x 2p 2p p A2 x 2 Integration of fX (x; t) gives the distribution function FX (x; t) 2 3 0, x A x 1 61 7 FX (x; t) ¼ 4 sin1 þ , A , x A 5 p A 2 1, x.A and these two solutions are exactly the same as before. For this random process, we find that the density and the distribution functions are both independent of time. For A ¼ 1, they become 1 fX (x; t) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi , 1 , x 1 p 1 x2 2 3 0, x 1 6 sin1 (x) 7 1 FX (x; t) ¼ 6 þ , 1 , x 1 7 4 5 p 2 1, x.1 and these functions are shown in Fig. 19.1.8. Since fX (x) has even symmetry, the mean value is 0 and the variance is obtained from ð1 x2 1 pffiffiffiffiffiffiffiffiffiffiffiffiffi dx ¼ s2X ¼ 2 2 1 p 1 x pffiffiffi The value of s ¼ +1= 2 is also shown in Fig. 19.1.8. Later we will discuss random processes whose density and distribution functions are independent of time.
FIGURE 19.1.8
414
Random Processes
Higher-Order Distribution Functions
If t1 and t2 are different times, then X1 ¼ X(t1) and X2 ¼ X(t2) are two different random variables as shown in Fig. 19.1.9. A second-order distribution function FX (x1 , x2 ; t1 , t2 ) for X1 and X2 can be defined as FX (x1 , x2 ; t1 , t2 ) ¼ P½X(t1 ) x1 , X(t2 ) x2
(19:1:6)
and the second-order density function fX (x1 , x2 ; t1 , t2 ) as fX (x1 , x2 ; t1 , t2 ) ¼
@2 FX (x1 , x2 ; t1 , t2 ) @x1 @x2
(19:1:7)
Similarly, if t1, . . . , tn are different times, then an nth-order distribution function is defined as FX (x1 , . . . , xn ; t1 , . . . , tn ) ¼ P½X(t1 ) x1 , . . . , X(tn ) xn
(19:1:8)
and the nth-order density function as fX (x1 , . . . , xn ; t1 , . . . , tn ) ¼
@n FX (x1 , . . . , xn ; t1 , . . . , tn ) @x1 @xn
(19:1:9)
A random variable is completely defined if its distribution function is known. Similarly, a random process X(t) is completely defined if its nth-order distribution is known for all n. Since this is not feasible for all n, a random process in general cannot be completely defined. Hence, we usually restrict the definition to second-order distribution function, in which case it is called a second-order process. In a similar manner, we can define a joint distribution between two different random processes X(t) and Y(t) (Fig. 19.1.10) as given below, where the joint second-order distribution function FXY (x1 , y2 ; t1 , t2 ) for X(t1) and Y(t2) is defined by FXY (x1 , y2 ; t1 , t2 ) ¼ P½X(t1 ) x1 , Y(t2 ) y2
(19:1:10)
and the joint density function fXY (x1 , y2 ; t1 , t2 ) as fXY (x1 , y2 ; t1 , t2 ) ¼
@2 FXY (x1 , y2 ; t1 , t2 ) @x1 @y2
FIGURE 19.1.9
(19:1:11)
19.1
Basic Definitions
415
FIGURE 19.1.10
The joint nth-order distribution function FXY (x1 , y2 ; t1 , t2 ) for X(t) and Y(t) is defined by FXY (x1 , . . . , xn : y1 , . . . , yn ; t1 , . . . , tn ) ¼ P½X(t1 ) x1 , . . . , X(tn ) xn : Y(t1 ) y1 , . . . , Y(tn ) yn
(19:1:12)
and the corresponding nth-order density function, by fXY (x1 , . . . , xn : y1 , . . . , yn ; t1 , . . . , tn ) ¼
@2n FXY (x1 , . . . , xn : y1 , . . . , yn ; t1 , . . . , tn ) @x1 @xn @y1 @yn
(19:1:13)
Second Order Moments
In Eqs. (19.1.3) and (19.1.4) defined mean and variance for a random process. The second moment of a random process has also been defined in Eq. (19.1.5). Since X(t1) and X(t2) are random variables, various types of joint moments can be defined. Autocorrelation. The autocorrelation function (AC) RX (t1, t2) is defined as the expected value of the product X(t1) and X(t2): ð1 ð1 x1 x2 fX (x1 , x2 ; t1 , t2 )dx1 dx2 (19:1:14) RX (t1 , t2 ) ¼ E½X(t1 )X(t2 ) ¼ 1 1
By substituting t1 ¼ t2 ¼ t in Eq. (19.1.14), we can obtain the second moment or the average power of the random process: ð1 2 RX (t) ¼ E ½X (t) ¼ x2 fX (x; t)dx (19:1:15) 1
Autocovariance. The autocovariance function (ACF) CX (t1, t2) is defined as the covariance between X(t1) and X(t2): CX (t1 , t2 ) ¼ E ½(X(t1 ) mX (t1 ))(X(t2 ) mX (t2 )) ð1 ð1 ¼ (x1 mX (t1 ))(x2 mX (t2 )) fX (x1 , x2 ; t1 , t2 )dx2 dx1 1 1
¼ E½X(t1 )X(t2 ) mX (t1 )mX (t2 )
(19:1:16)
416
Random Processes
Thus, from Eqs. (19.1.15) and (19.1.16) the interrelationships between AC and ACF are CX (t1 , t2 ) ¼ RX (t1 , t2 ) mX (t1 )mX (t2 ) RX (t1 , t2 ) ¼ CX (t1 , t2 ) þ mX (t1 )mX (t2 )
(19:1:17)
Normalized Autocovariance. The normalized autocovariance function (NACF) rX (t1, t2) is the ACF normalized by the variance and is defined by CX (t1 , t2 ) CX (t1 , t2 ) rX (t1 , t2 ) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ s2X (t1 )s2X (t2 ) sX (t1 )sX (t2 )
(19:1:18)
The NACF finds wide applicability in many problems in random processes, particularly in time-series analysis. The following definitions pertain to two different random processes X(t) and Y(t): Cross-Correlation. The cross-correlation function (CC) RXY (t1, t2) is defined as the expected value of the product X(t1) and Y (t2): ð1 ð1 RXY (t1 , t2 ) ¼ E½X(t1 )Y(t2 ) ¼ x1 y2 fXY (x1 , y2 ; t1 , t2 )dy2 dx1 (19:1:19) 1
1
By substituting t1 ¼ t2 ¼ t in Eq. (19.1.19), we can obtain the joint moment between the random processes X(t) and Y(t) as ð1 ð1 RXY (t) ¼ E½X(t)Y(t) ¼ xy fXY (x, y; t)dydx (19:1:20) 1
1
Cross-Covariance. The cross-covariance function (CCF) CXY (t1, t2) is defined as the covariance between X(t1) and Y(t2): CXY (t1 , t2 ) ¼ E½(X(t1 ) mX (t1 ))(Y(t2 ) mY (t2 )) ð1 ð1 ¼ (x1 mX (t1 ))(y2 mY (t2 )) fXY (x1 ,y2 ; t1 , t2 )dy2 dx1 1 1
¼ E½X(t1 )Y(t2 ) mX (t1 )mY (t2 )
(19:1:21)
Thus, from Eqs. (19.1.19) and (19.1.21) the interrelationships between CC and CCF are CXY (t1 , t2 ) ¼ RXY (t1 , t2 ) mX (t1 )mY (t2 ) RXY (t1 , t2 ) ¼ CXY (t1 , t2 ) þ mX (t1 )mY (t2 )
(19:1:22)
Normalized Cross-Covariance. The normalized cross-covariance function (NCCF) rXY(t1, t2) is the CCF normalized by the variances and is defined by CXY (t1 , t2 ) CXY (t1 , t2 ) rXY (t1 , t2 ) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 2 2 s X (t1 )sY (t2 ) sX (t1 )sY (t2 )
(19:1:23)
Some Properties of X(t) and Y(t)
Two random processes X(t) and Y(t) are independent if for all t1 and t2 FXY (x, y; t1 , t2 ) ¼ FX (x; t1 )FY (y; t2 )
(19:1:24)
19.1
Basic Definitions
417
They are uncorrelated if CXY (t1 , t2 ) ¼ RXY (t1 , t2 ) mX (t1 )mY (t2 ) ¼ 0 or RXY (t1 , t2 ) ¼ mX (t1 )mY (t2 )
for all t1 and t2
(19:1:25)
They are orthogonal if for all t1 and t2 RXY (t1 , t2 ) ¼ 0
(19:1:26)
Example 19.1.6 This example is slightly different from Example 19.1.4. A random process X(t) is given by X(t) ¼ A sin(vt þ f) as shown in Fig. 19.1.11, where A is a uniformly distributed random variable with mean mA and variance s2A. We will find the mean, variance, autocorrelation, autocovariance, and normalized autocovariance of X(t). Mean: E½X(t) ¼ mX (t) ¼ E ½A sin(vt þ f) ¼ mA sin(vt þ f) Variance: var½X(t) ¼ s2X (t) ¼ E½A2 sin2 (vt þ f) m2A sin2 (vt þ f) ¼ {E ½A2 m2A } sin2 (vt þ f) ¼ s2A sin2 (vt þ f) Autocorrelation. From Eq. (19.1.14) we have RX (t1 , t2 ) ¼ E½X(t1 )X(t2 ) ¼ E ½A2 sin(vt1 þ f) sin(vt2 þ f) 1 ¼ E ½A2 {cos½v(t1 t2 ) cos½v(t1 þ t2 ) þ 2f} 2
FIGURE 19.1.11
418
Random Processes
Autocovariance. From Eq. (19.1.16) we have CX (t1 , t2 ) ¼ RX (t1 , t2 ) mX (t1 )mX (t2 ) ¼ E½A2 sin(vt1 þ f) sin(vt2 þ f) m2A sin(vt1 þ f) sin(vt2 þ f) ¼ s2A sin(vt1 þ f) sin(vt2 þ f) 1 ¼ s2A {cos½v(t1 t2 ) cos½v(t1 þ t2 ) þ 2f} 2 Normalized Autocovariance. From Eq. (19.1.18) we have rX (t1 , t2 ) ¼
CX (t1 , t2 ) s2 sin(vt1 þ f) sin(vt2 þ f) ¼ A2 ¼1 sX (t1 )sX (t2 ) sA sin(vt1 þ f) sin(vt2 þ f)
Example 19.1.7 A random process X(t) with k changes in a time interval t, and its probability mass function is given by p(k; l) ¼ elt ½(lt)k =k!. It is also known that the joint probability Pfk1 changes in t1, k2 changes in t2g is given by P{k1 changes in t1 , k2 changes in t2 } ¼ P{k1 changes in t1 , (k2 k1 ) changes in (t2 t1 )} ¼ P{k1 changes in t1 }P{(k2 k1 ) changes in (t2 t1 )} ¼ elt1
(lt1 )k1 l(t2 t1 ) ½l(t2 t1 )(k2 k1 ) e k1 ! (k2 k1 )!
We have to find the mean, variance, autocorrelation, autocovariance, and normalized autocovariance of X(t): Mean: E ½X(t) ¼ mX (t) ¼
1 X
kelt
k¼0
(lt)k ¼ lt k!
Variance: var½X(t) ¼ s2X (t) ¼
1 X
k2 elt
k¼0
(lt)k (lt)2 ¼ lt k!
Autocorrelation. From Eq. (19.1.18) we have RX (t1 , t2 ) ¼ E½X(t1 )X(t2 ) ¼ E{X(t1 )½X(t2 ) X(t1 ) þ X(t1 )} ¼ E ½X 2 (t1 ) þ E ½X(t1 )E½X(t2 ) X(t1 ) (from condition given) ¼ (lt1 )2 þ lt1 þ lt1 l(t2 t1 ) ¼ l2 t1 t2 þ lt1 if t2 . t1 and we have a similar result if t1 . t2: RX (t1 , t2 ) ¼ l2 t1 t2 þ lt2
if
t1 . t2
19.1
Basic Definitions
419
Combining these two results, we have RX (t1 , t2 ) ¼ l2 t1 t2 þ l min (t1 , t2 ) Autocovariance. From Eq. (19.1.19) we have CX (t1 , t2 ) ¼ RX (t1 , t2 ) mX (t1 )mX (t2 ) ¼ l2 t1 t2 þ l min (t1 , t2 ) l2 t1 t2 ¼ l min (t1 , t2 ) Normalized Autocovariance. From Eq. (19.1.18) we have CX (t1 , t2 ) min (t1 , t2 ) 1 pffiffiffiffiffiffiffi ¼ min rX (t1 , t2 ) ¼ ¼ sX (t1 )sX (t2 ) l t1 t2 l
Example 19.1.8
rffiffiffiffi rffiffiffiffi t1 t2 , t2 t1
Two random processes X(t) and Y(t) are given by X(t) ¼ A sin(vt þ f1 );
Y(t) ¼ B sin(vt þ f2 )
where A and B are two random variables with parameters E[A] ¼ mA, E[B] ¼ mB, var[A] ¼ s2A, var[B] ¼ s2B, cov[AB] ¼ sAB, and correlation coefficient rAB ¼ sAB = (sA sB ). We have to find the means and variances of X(t) and Y(t) and their crosscorrelation, cross-covariance, and normalized cross-covariance. The means and variances can be obtained directly from Example 19.1.5. Means: mX (t) ¼ E½A sin(vt þ f1 ) ¼ mA sin(vt þ f1 ) mY (t) ¼ E ½B cos(vt þ f2 ) ¼ mB cos(vt þ f2 ) Variances: s2X (t) ¼ s2A sin2 (vt þ f1 ) s2Y (t) ¼ s2B cos2 (vt þ f2 ) Cross-Correlation: RXY (t1 , t2 ) ¼ E½X(t1 )Y(t2 ) ¼ E½AB sin(vt1 þ f1 ) cos(vt2 þ f2 ) 1 ¼ E ½AB{sin½v(t1 þ t2 ) þ f1 þ f2 þ sin½v(t1 t2 ) þ f1 f2 } 2 Cross-Covariance: CXY (t1 ,t2 ) ¼ RXY (t1 ,t2 ) mX (t1 )mY (t2 ) ¼ E ½ABsin(vt1 þ f1 )cos(vt2 þ f2 ) mA mB sin(vt1 þ f1 )cos(vt2 þ f2 ) ¼ sAB sin(vt1 þ f1 )cos(vt2 þ f2 ) 1 ¼ sAB {sin½v(t1 þ t2 ) þ f1 þ f2 þ sin½v(t1 t2 ) þ f1 f2 } 2 Normalized Cross-Covariance: rXY (t1 , t2 ) ¼
CXY (t1 , t2 ) sAB sin(vt1 þ f1 ) cos(vt2 þ f2 ) sAB ¼ ¼ ¼ rAB sX (t1 )sY (t2 ) sA sB sin(vt1 þ f1 ) cos(vt2 þ f2 ) sA sB
420
Random Processes
B 19.2
STATIONARY RANDOM PROCESSES
The distribution functions of all the random processes except that in Example 19.1.4 were dependent on time. However, many of the random processes such as Example 19.1.4 have the important property that their statistics do not change with time, which is an important step toward obtaining the statistics from a single sample function. The statistics in the time interval (t1, t2) is the same as in the time interval (t1 þ t, t2 þ t). In other words, the probabilities of the samples of a random process X(t) at times t1, . . . , tn will not differ from those at times t1 þ t, . . . , tn þ t. This means that the joint distribution function of X(t1), . . . , X(tn) is the same as X(t1 þ t), . . . , X(tn þ t), or FX (x1 , . . . , xn ; t1 , . . . , tn ) ¼ FX (x1 , . . . , xn ; t1 þ t, . . . , tn þ t)
(19:2:1)
and the corresponding density function may be written as fX (x1 , . . . , xn ; t1 , . . . , tn ) ¼ fX (x1 , . . . , xn ; t1 þ t, . . . , tn þ t)
(19:2:2)
Random processes with the property of Eq. (19.2.1) or (19.2.2) are called nth-order stationary processes. A strict-sense or strongly stationary process is a random process that satisfies Eqs. (19.2.1) and (19.2.2) for all n. Analogously, we can also define lower orders of stationarity. A random process is first-order stationary if FX (x; t) ¼ FX (x; t þ t) ¼ FX (x) fX (x; t) ¼ fX (x; t þ t) ¼ fX (x)
(19:2:3)
and the distribution and density functions are independent of time. The random process in Examples 19.1.2 and 19.1.5 is an example of a first-order stationary process. A random process is second-order stationary if FX (x1 , x2 ; t1 , t2 ) ¼ FX (x1 , x2 ; t1 þ t, t2 þ t) ¼ FX (x1 , x2 ; t) fX (x1 , x2 ; t1 , t2 ) ¼ fX (x1 , x2 ; t1 þ t, t2 þ t) ¼ fX (x1 , x2 ; t)
(19:2:4)
The distribution and density functions are dependent not on two time instants t1 and t2 but on the time difference t ¼ t1 – t2 only. Second-order stationary processes are also called wide-sense stationary or weakly stationary. Hereafter, stationary means wide-sense stationary, and strict-sense stationary will be specifically mentioned. In a similar manner, two processes X(t) and Y(t) are jointly stationary if for all n FXY (x1 , . . . , xn ; y1 , . . . , yn ; t1 , . . . , tn ) ¼ FXY (x1 , . . . , xn ; y1 , . . . , yn ; t1 þ t, . . . , tn þ t)
(19:2:5)
fXY (x1 , . . . , xn ; y1 , . . . , yn ; t1 , . . . , tn ) ¼ fXY (x1 , . . . , xn ; y1 , . . . , yn ; t1 þ t, . . . , tn þ t)
(19:2:6)
or
and they are jointly wide-sense stationary if FXY (x1 , y2 ; t1 , t2 ) ¼ FXY (x1 , y2 ; t1 þ t, t2 þ t) ¼ FXY (x1 , y2 ; t)
(19:2:7)
19.2
Stationary Random Processes
421
and the joint density function fXY (x1 , y2 ; t1 , t2 ) ¼ fXY (x1 , y2 ; t1 þ t, t2 þ t) ¼ fXY (x1 , y2 ; t)
(19:2:8)
nth-order stationarity implies lower-order stationarities. Strict-sense stationarity implies wide-sense stationarity. Similar to Eqs. (19.1.24) –(19.1.26), we can enumerate the following properties for stationary random processes X(t) and Y(t). Two random processes X(t) and Y(t) are independent if for all x and y FXY (x, y) ¼ FX (x)FY (y)
(19:2:9)
They are uncorrelated if for all t CXY (t) ¼ RXY (t) mX mY ¼ 0 or RXY (t) ¼ mX mY
(19:2:10)
They are orthogonal if for all t RXY (t) ¼ 0
(19:2:11)
Moments of Continuous-Time Stationary Processes
We can now define the various moments for a stationary random process X(t). Mean: ð1 E ½X(t) ¼ mX ¼
xf (x)dx
(19:2:12)
1
Variance: 2
E ½X(t) mX ¼
s2X
ð1 ¼
(x mX )2 f (x)dx ¼ E½X 2 (t) m2X
(19:2:13)
1
Autocorrelation: ð1 ð1 RX (t) ¼ E ½X(t)X(t þ t) ¼
x1 x2 f (x1 , x2 ; t)dx1 dx2
(19:2:14)
1 1
Autocovariance: CX (t) ¼ E{½X(t) mX ½X(t þ t) mX } ð1 ð1 (x1 mX )(x2 mX ) f (x1 , x2 ; t)dx1 dx2 ¼ 1
(19:2:15)
1
¼ RX (t) m2X Normalized Autocovariance (NACF): rX (t) ¼
CX (t) s2X
(19:2:16)
422
Random Processes
White-Noise Process
A zero mean stationary random process X(t) whose autocovariance or autocorrelation is given by CX (t) ¼ RX (t) ¼ s2X d(t)
(19:2:17)
where d(t) is the Dirac delta function, is called a white-noise process. The energy of a white-noise process is infinite since CX (0) ¼RX (0) ¼ E[X 2(t)] ¼ 1. Hence it is an idealization. White-noise processes find extensive use in modeling communication systems. The cross-moments of two jointly stationary processes X(t) and Y(t) are defined below: Cross-Correlation: ð1 ð1 RXY (t) ¼ E ½X(t)Y(t þ t) ¼
x1 y2 f (x1 , y2 ; t)dy2 dx1 1
(19:2:18)
1
Cross-Covariance: CXY (t) ¼ E{½X(t) mX ½Y(t þ t) mY } ð1 ð1 (x1 mX )(y2 mY ) f (x1 , y2 ; t)dy2 dx1 ¼ 1 1
¼ RXY (t) mX mY
(19:2:19)
Normalized Cross-Covariance: rXY (t) ¼
CXY (t) sX sY
(19:2:20)
A stationary random process X(t) is passed through a linear system with impulse response h(t). The input – output relationship will be given by the convolution integral: ð1 ð1 Y(t) ¼ X(t a)h(a)da ¼ X(t)h(t a)da (19:2:21) 1
1
The cross-correlation function RXY (t) between the input and the output can be found as follows: ð1 E½X(t)Y(t þ t) ¼ E X(t)X(t þ t a)h(a)da (19:2:22) 1
or ð1 RX (t a)h(a)da
RXY (t) ¼
(19:2:23)
1
Since the cross-correlation function depends only on t, the output Y(t) will also be a stationary random process. Properties of Correlation Functions of Stationary Processes
Autocorrelation Functions 1. RX (0) ¼ E[X 2(t)] ¼ average power 0 2. RX (t) ¼ RX (t). Or, RX (t) is an even function. RX (t) ¼ E½X(t)X(t þ t) ¼ E ½X(t þ t)X(t) ¼ RX (t)
(19:2:24)
19.2
Stationary Random Processes
423
3. jRX (t)j RX (0). Or, the maximum value of jRX (t)j occurs at t ¼ 0 and jRX (t)j for any t cannot exceed the value RX (0). From E½X(t) + X(t þ t)2 0 we have E ½X 2 (t) þ E ½X 2 (t þ t) + 2E ½X(t)X(t þ t) 0 or RX (0) jRX (t)j
(19:2:25)
4. If a constant T . 0 exists such that RX (T ) ¼ RX (0), then the RX (t) is periodic and X(t) is called a periodic stationary process. From Schwartz’ inequality [Eq. (14.5.13)] we have {E ½g(X)h(X)}2 E ½g2 (X)E½h2 (X) Substituting g(X ) ¼ X(t) and h(X) ¼ ½X(t þ t þ T ) X(t þ t), we can write {E½X(t)½X(t þ t þ T ) X(t þ t)}2 E½X 2 (t)E½X(t þ t þ T ) X(t þ t)2 or {RX (t þ T ) RX (t)}2 2RX (0)½RX (0) RX (T ) Hence, if RX (T ) ¼ RX (0), then, since {RX (t þ T ) RX (t)}2 0, the result RX (t þ T ) ¼ RX (t) follows. 5. E ½X(t þ f)X(t þ t þ f) ¼ RX (t) ¼ E½X(t)X(t þ t) E ½X(t þ f)Y(t þ t þ f) ¼ RXY (t) ¼ E½X(t)Y(t þ t) In the formulation of correlation functions the phase information is lost. 6. If E[X(t)] ¼ mX and Y(t) ¼ a þ X(t), where a is constant, then E[Y(t)] ¼ a þ mX and, RY (t) ¼ E{½a þ X(t)½a þ X(t þ t)} ¼ a2 þ aE ½X(t þ t) þ aE ½X(t) þ E ½X(t)X(t þ t) ¼ a2 þ 2amX þ RX (t) ¼ a2 þ 2amX þ m2X þ CX (t) ¼ (a þ mX )2 þ CX (t)
(19:2:26)
If E [X(t)] ¼ 0, then E[Y(t)] ¼ a, and we can obtain the mean value of Y(t) from a knowledge of its autocorrelation function RY(t).
Cross-Correlation Functions
7. RXY (t) ¼ RYX (t): This is not an even function:
ð19:2:27Þ
424
Random Processes
The result follows from the definition of cross-correlation: RXY (t) ¼ E½X(t)Y(t þ t) ¼ E½Y(t þ t)X(t) ¼ RYX (t)
but
RXY (0) ¼ RYX (0) ð19:2:28Þ
8. R2XY (t) RX (0)RY (0): This result follows from Schwartz’ inequality: {E ½X(t)Y(t þ t)}2 E ½X 2 (t) E ½Y 2 (t þ t) 9. 2jRXY (t)j RX (0) þ RY (0): The result follows from E ½X(t) + Y(t þ t)2 ¼ RX (0) þ RY (0) + 2RXY (t) 0
(19:2:29)
10. If Z(t) ¼ X(t) þ Y(t), then RZ (t) ¼ E ½Z(t)Z(t þ t) ¼ E{½X(t) þ Y(t)½X(t þ t) þ Y(t þ t)} ¼ RX (t) þ RY (t) þ RXY (t) þ RYX (t)
(19:2:30)
and if X(t) and Y(t) are orthogonal, then RZ (t) ¼ RX (t) þ RY (t). _ 11. If X(t) is the derivative of X(t), then the cross-correlation between X(t) and _X(t) is RX X_ (t) ¼
dRX (t) dt
(19:2:31a)
This result can be shown from the formal definition of the derivative of X(t). Substituting for X_ ¼ lim1!0 {½X(t þ 1) X(t)=1}, we obtain RX X_ (t) ¼ E½X(t)X(t þ t) X(t þ t þ 1) X(t þ t) ¼ lim E X(t) 1!0 1 X(t)X(t þ t þ 1) X(t)X(t þ t) ¼ lim E 1!0 1 RX (t þ 1) RX (t) dRX (t) ¼ 1!0 dt 1
¼ lim
_ is 12. The autocorrelation of X(t)
RX_ (t) ¼
d2 RX (t) dt2
(19:2:31b)
19.2
Stationary Random Processes
425
This result can be shown following a procedure similar to that described as in (11) above: _ X(t _ þ t) RX_ (t) ¼ E½X(t) X(t þ t þ 1) X(t þ t) ¼ lim E X(t) 1!0 1 X(t þ d) X(t) X(t þ t þ 1) X(t þ t) ¼ lim lim E d!0 1!0 d 1 2 3 X(t þ t þ 1) X(t þ t) X(t þ d) 1 6 7 1 ¼ lim lim E4 X(t þ t þ 1) X(t þ t) 5 d!0 d 1!0 X(t) 1 1 RX (t d þ 1) RX (t d) RX (t þ 1) RX (t) ¼ lim lim d!0 d 1!0 1 1 1 dRX (t d) dRX (t) d2 RX (t) ¼ lim ¼ d!0 d dt dt dt2 It can be shown that if a random process is wide-sense stationary, then it is necessary and sufficient that the following two conditions be satisfied: 1. The expected value is a constant, E [X(t)] ¼ mX. 2. The autocorrelation function RX is a function of the time difference t2 2 t1 ¼ t and not individual times, RX (t1 , t2 ) ¼ RX (t2 t1 ) ¼ RX (t). We will now give several examples to establish conditions for stationarity. The first few examples will be running examples that become progressively more difficult.
Example 19.2.1 We will revisit Example 19.1.5 and find the conditions necessary for the random process X(t) ¼ A sin(vt þ F) to be stationary where A,v are constants and F is a random variable: 1. Mean: ðb E½X(t) ¼ AE½sin(vt þ F) ¼ A
sin(vt þ f) fF (f)df a
One of the ways the integral will be independent of t is for F to be uniformly distributed in (0,2p), in which case we have 1 2p and E [X(t)] ¼ 0.
ð 2p sin(vt þ f)df ¼ 0
2p 1
cos (vt þ f) ¼ 0 0 2p
426
Random Processes
2. Autocorrelation: E ½X(t1 )X(t2 ) ¼ A2 E ½sin(vt1 þ F) sin(vt2 þ F) ¼ A2 E{cos½v(t2 t1 ) cos½v(t2 þ t1 ) þ 2F} ¼ A2 cos½v(t2 t1 ) A2 E{cos½v(t2 þ t1 ) þ 2F} ð 1 2p cos½v(t2 þ t1 ) þ 2f df E{cos½v(t2 þ t1 ) þ 2F} ¼ 2p 0
2p 1 1
sin½v(t2 þ t1 ) þ 2f ¼ 0 ¼ 0 2p 2 Hence, RX (t1 , t2 ) ¼ A2 cos½v(t2 t1 ) ¼ A2 cos½vt, and X(t) is stationary if F is uniformly distributed in (0,2p). Since RX(t) is periodic, X(t) is a periodic stationary process. Example 19.2.2 We will modify Example 19.2.1 with both A and F as random variables with density functions fA(a) and fF(f). We will now find the conditions under which X(t) ¼ A sin(vt þ F) will be stationary. 1. Mean: ðð E ½X(t) ¼ E ½A sin(vt þ F) ¼
a sin(vt þ f) fAF (a, f)df da
The first condition for the double integral to be independent of t is for A and F to be statistically independent, in which case we have ðð E ½A sin(vt þ F) ¼ a sin(vt þ f) fA (a) fF df da and the second condition Ð 2p is for F to be uniformly distributed in (0,2p), in which case we have (1=2p) 0 sin(vt þ f) df ¼ 0 and E ½X(t) ¼ 0. 2. Autocorrelation: E ½X(t1 )X(t2 ) ¼ E ½A2 sin(vt1 þ F) sin(vt2 þ F) Since A and F are independent, we have E ½X(t1 )X(t2 ) ¼ E ½A2 E{cos½v(t2 t1 ) cos½v(t2 þ t1 ) þ 2F} ¼ E ½A2 cos½v(t2 t1 ) E ½A2 E{cos½v(t2 þ t1 ) þ 2F} and from the previous example E{cos½v(t2 þ t1 ) þ 2F} ¼ 0. Hence, RX (t1 , t2 ) ¼ E ½A2 cos½v(t2 t1 ) ¼ E ½A2 cos½vt and X(t) is stationary if A and F are independent and if F is uniformly distributed in (0,2p). Since RX(t) is periodic, X(t) a periodic stationary process. Example 19.2.3 We will now examine the conditions for stationarity for X(t) ¼ A sin(Vt þ F) when A, V and F are all random variables with density functions fA(a), fV(v), and fF(f) respectively.
19.2
Stationary Random Processes
427
1. Mean: ððð E ½X(t) ¼ E ½A sin(Vt þ F) ¼
a sin(vt þ f) fAVF (a,v,f)df dv da
This triple integral is a difficult one to evaluate. Hence we resort to conditionalÐ expectations by fixing the variable V ¼ v. Under this condition, E ½X(t) ¼ E ½X(t)jV ¼ v fV (v)dv and from the previous example, if A and F are independent and F is uniformly distributed in (0, 2p), we have E ½X(t)jV ¼ v ¼ 0, and ð
ð E ½X(t)jV ¼ v fV (v)dv ¼ E ½A sin(vt þ F) fV (v)dv ¼ 0
2. Autocorrelation: RX (t1 , t2 jV ¼ v) ¼ E ½X(t1 jV ¼ v)X(t2 jV ¼ v) ¼ E ½A2 sin(vt1 þ F) sin(vt2 þ F) and from the previous example 1 E ½A2 sin(vt1 þ F) sin(vt2 þ F) ¼ E ½A2 cos½v t 2 where t ¼ t2 t1 . Hence ð ð 1 E ½A2 cos½vt fV (v)dv RX (t) ¼ RX (tjV ¼ v) fV (v)dv ¼ 2 If V is uniformly distributed in (0,p), then
RX (t) ¼
1 p
ðp 0
1 E ½A2 sin(p) E ½A2 E ½A2 cos½vtdv ¼ ¼ Sa(p) 2 2p t 2
Example 19.2.4 In this example X(t) ¼ A cos(vt) þ B sin(vt), where A and B are random variables with density functions fA(a) and fB(b). We have to find the conditions under which X(t) will be stationary: 1. Mean: E ½X(t) ¼ E ½A cos(vt) þ B sin(vt) ¼ cos(vt)E ½A þ sin(vt)E ½B If E [X(t)] is to be independent of t, then E[A] ¼ E[B] ¼ 0, in which case E [X(t)] ¼ 0.
428
Random Processes
2. Autocorrelation: RX (t1 , t2 ) ¼ E ½X(t1 )X(t2 ) ¼ E{½A cos(vt1 ) þ B sin(vt1 )½A cos(vt2 ) þ B sin(vt2 )} ¼ E{A2 cos(vt1 ) cos(vt2 ) þ B2 sin(vt1 ) sin(vt2 ) þ AB½sin(vt1 ) cos(vt2 ) þ cos(vt1 ) sin(vt2 )} 1 ¼ E ½A2 ½cos(v(t2 t1 )) þ cos(v(t2 þ t1 )) 2 1 þ E ½B2 ½cos(v(t2 t1 )) cos(v(t2 þ t1 )) 2 þ E ½AB sin(v(t2 þ t1 )) The conditions under which this equation will be dependent only on (t2 t1 ) are E[A 2] ¼ E[B 2] and E[AB] ¼ 0. In this case RX (t1 ,t2 ) ¼ E ½A2 ½cos(v(t2 t1 )) ¼ E ½A2 ½cos(vt) We can now summarize the conditions for stationarity: (a) E[A] ¼ E[B] ¼ 0 (b) E[A 2] ¼ E[B 2] (c) E[AB] ¼ 0 Example 19.2.5 A die is tossed, and corresponding to the dots S ¼ f1,2,3,4,5,6g, a random process X(t) is formed with the following time functions as shown in Fig. 19.2.1: X(2;t) ¼ 3, X(4; t) ¼ (2 t), X(6; t) ¼ (1 þ t) X(1; t) ¼ 3, X(3;t) ¼ (2 t), X(5;t) ¼ (1 þ t)
FIGURE 19.2.1
19.2
Stationary Random Processes
429
We have to find mX (t), s2X (t), RX (t1 ,t2 ), CX (t1 , t2 ), and rX (t1 , t2 ) and check whether X(t) is stationary: Mean: mX (t) ¼
6 1X Xi (t) ¼ 3 3 þ (2 t) (2 t) þ (1 þ t) (1 þ t) ¼ 0 6 i¼1
The mean value is a constant. Variance: s2X (t) ¼
6 1X 1 2 Xi2 (t) ¼ ½32 þ (2 t)2 þ (1 þ t)2 ¼ ½t2 t þ 7 6 i¼1 3 3
Autocorrelation: 1 1 RX (t1 , t2 ) ¼ ½9 þ (1 þ t1 )(1 þ t2 ) þ (2 t1 )(2 t2 ) ¼ ½14 t2 t1 þ 2t1 t2 3 3 Autocovariance. Since the mean value is zero, CX (t1 , t2 ) ¼ RX (t1 , t2 ). Normalized Autocovariance: CX (t1 , t2 ) ½14 t2 t1 þ 2t1 t2 rX (t1 , t2 ) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi CX (t1 )CX (t2 ) (t12 t1 þ 7)(t22 t2 þ 7) This process is not stationary.
Example 19.2.6 (Random Binary Wave) A sample function of a random binary wave X(t) consisting of independent rectangular pulses p(t), each of which is of duration T, is shown in Fig. 19.2.2. The height H of the pulses is a random variable with constant amplitudes, which are equally likely to be +A. The time of occurrence of X(t) after t ¼ 0 is another random variable Z, which is uniformly distributed in (0,T ). We have to find the mean, variance, autocorrelation, autocovariance, and the normalized autocovariance of X(t). The random process X(t) is given by X(t) ¼
1 X
Hp(t kT Z)
k¼1
FIGURE 19.2.2
430
Random Processes
The probabilities of the random variable H are P(H ¼ A) ¼ 12 and P(H ¼ A) ¼ 12. Since Z is uniformly distributed in (0,T ), the following probabilities can be formulated for 0 , t1 , t2 , T: P(t1 , Z , t2 ) ¼
t2 t1 , T
P(0 , Z , t1 ) ¼
t1 , T
P(t2 , Z , T ) ¼ 1
t2 T
Mean. Since X(t) assumes only þA or 2A with equal probability, E ½X(t) ¼
1 1 A þ (A) ¼ 0 2 2
Variance. The variance is given by var½X(t) ¼
s2X
1 2 1 2 A þ (A) ¼ A2 ¼ E ½X (t) ¼ 2 2 2
Autocorrelation. Determining RX (t1 , t2 ) is a bit more involved. The product X(t1)X(t2) in various intervals of time (t1, t2) can be found: t1 , Z , t2 T Here t1 and t2 lie in adjacent pulse intervals as shown in Fig. 19.2.3. In Fig. 19.2.3a X(t1) ¼ 2A and X(t2) ¼ þA and X(t1)X(t2) ¼ 2A 2. In Fig. 19.2.3b X(t1) ¼ þA and X(t2) ¼ þA and X(t1)X(t2) ¼ A 2. The values 2A 2 and A 2 will occur with equal probability: 0 , Z , t1 T: In this case, t1 and t2 lie in the same pulse interval as shown in Fig. 19.2.3c. Here, X(t1) ¼ þA and X(t2) ¼ þA and X(t1)X(t2) ¼ A 2. t2 , Z , T: Here also t1 and t2 lie in the same pulse interval as shown in Fig. 19.2.3d. However, X(t1) ¼ 2A and X(t2) ¼ 2A and X(t1)X(t2) ¼ A 2. (t1 2 t2) . T: In this case t1 and t2 lie in different pulse intervals and X(t1)X(t2) ¼ +A 2. We can now find the autocorrelation function RX (t1 , t2 ) ¼ E ½X(t1 )X(t2 ). For (t1 2 t2) . T, X(t1) and X(t2) are in different pulse intervals and invoking the independence of the pulses E [X(t1)X(t2)] ¼ E [X(t1)]E [X(t2)] ¼ 0 since E [X(t)] ¼ 0. For (t1 2 t2) T, we have the following, using conditional expectations: E ½X(t1 )X(t2 ) ¼ E ½X(t1 )X(t2 )jt1 , Z , t2 P(t1 , Z , t2 ) þ E ½X(t1 )X(t2 )j0 , Z , t1 P(0 , Z , t1 ) þ E ½X(t1 )X(t2 )jt1 , Z , TP(t1 , Z , T ) The conditional expectations can be determined using the probabilities and the products X(t1)X(t2) found earlier for the various intervals. Since A 2 and 2A 2 occur with equal probability in the interval t1 , Z , t2 T, the first conditional expectation term becomes 1 t2 t1 1 t2 t1 A2 ¼0 E ½X(t1 )X(t2 )jt1 , Z , t2 P(t1 , Z , t2 ) ¼ A2 2 2 T T
19.2
FIGURE 19.2.3
Stationary Random Processes
431
432
Random Processes
FIGURE 19.2.4
Hence " RX (t1 , t2 ) ¼
A2
t t2 t2 t1 1 þ1 ¼ A2 1 , T T T
(t2 t1 ) T (t2 t1 ) . T
0,
This equation was derived under the assumption t1 , t2. For arbitrary values of t1 and t2 with t ¼ (t2 2 t1), the autocorrelation function RX(t1, t2) is given by 8 jtj < 2 A 1 , jtj , T RX (t) ¼ T : 0, otherwise The autocorrelation function for the process X(t) is shown in Fig. 19.2.4. Autocovariance. Since the mean value is 0, CX(t) ¼ RX(t). Normalized Autocovariance. The NACF is given by ( jtj CX (t) 1 , jtj , T rX (t) ¼ 2 ¼ T sX 0; otherwise Example 19.2.7 (Random Telegraph Wave) This example is a little different from the previous one. A sample function of a random telegraph wave is shown in Fig. 19.2.5. The wave assumes either of the values 1 or 0 at any instant of time.
FIGURE 19.2.5
19.2
Stationary Random Processes
433
The probability of k changes from 0 to 1 in a time interval t is Poisson-distributed with probability mass function p(k; l) given by (lt)k lt e , k!
p(k; l) ¼
t0
where l is the average number of changes per unit time. We have to find the mean, variance, autocorrelation, autocovariance, and normalized autocovariance. Mean. Since X(t) assumes only 1 or 0 with equal probability, we obtain E ½X(t) ¼ mX ¼ 1 P(x ¼ 1) þ 0 P(x ¼ 0) ¼
1 2
Variance. The variance is given by var½X(t) ¼
s2X
2
¼ E ½X (t)
m2X
1 2 1 2 1 1 1 þ 0 ¼ ¼ 2 2 4 4
Autocorrelation. The autocorrelation function can be given in terms of the joint density functions with t2 . t1: RX (t1 , t2 ) ¼ E ½X(t1 ) ¼ 0 X(t2 ) ¼ 0 þ E ½X(t1 ) ¼ 0 X(t2 ) ¼ 1 þ E ½X(t1 ) ¼ 1 X(t2 ) ¼ 0 þ E ½X(t1 ) ¼ 1 X(t2 ) ¼ 1 ¼ (0 0)P½X(t1 ) ¼ 0 X(t2 ) ¼ 0 þ (0:1)P½X(t1 ) ¼ 0 X(t2 ) ¼ 1 þ (1 0)P½X(t1 ) ¼ 1 X(t2 ) ¼ 0 þ (1:1)P½X(t1 ) ¼ 1 X(t2 ) ¼ 1 or RX (t1 , t2 ) ¼ P½X(t1 ) ¼ 1 X(t2 ) ¼ 1 Expressing the joint probability in terms of conditional probabilities, we have RX (t1 , t2 ) ¼ P½X(t2 ) ¼ 1 j X(t1 ) ¼ 1P½X(t1 ) ¼ 1 The conditional probability in this equation is the probability of even number of changes and using the Poisson distribution: RX (t1 , t2 ) ¼
1 1 X ½l(t2 t1 )k l(t2 t1 ) e 2 k¼0 k! k even
( ) 1 1 el(t2 t1 ) 1 X ½l(t2 t1 )k X ½l(t2 t1 )k þ ¼ 2 k¼0 2 k! k! k¼0 el(t2 t1 ) el(t2 t1 ) þ el(t2 t1 ) ¼ 2 2 1 ¼ {1 þ e2l(t2 t1 ) }, 4
t2 . t1
A similar equation holds good for t2 , t1. Hence, substituting jt2 t1 j ¼ jtj in the equation above, RX(t) can be given by 1 RX (t) ¼ {1 þ e2ljtj } 4
434
Random Processes
FIGURE 19.2.6
The autocorrelation function for the process X(t) is shown in Fig. 19.2.6 for l ¼ 0.5. Autocovariance: CX (t) ¼ RX (t) m2X ¼
e2ljtj 4
Normalized Autocovariance: rX (t) ¼
CX (t) ¼ e2ljtj s2X
Example 19.2.8 (Modulation) A random process Y(t) is given by Y(t) ¼ X(t) cos(vt þ F), where X(t), a zero mean wide-sense stationary random process with autocorrelation function RX (t) ¼ 2e2ljtj is modulating the carrier cos(vt þ F). The random variable F is uniformly distributed in the interval (0,2p), and is independent of X(t). We have to find the mean, variance, and autocorrelation of Y(t): Mean. The independence of X(t) and F allows us to write E ½Y(t) ¼ E ½X(t)E ½cos(vt þ F) and with E ½X(t) ¼ 0 and E ½cos(vt þ F) ¼ 0 from Example 19.2.1, E[Y(t)] ¼ 0. Variance. Since X(t) and F are independent, the variance can be given by s2Y ¼ E ½Y 2 (t) ¼ E ½X 2 (t) cos2 (vt þ F) ¼ s2X E ½cos2 (vt þ F) However 1 1 E ½cos2 (vt þ F) ¼ E ½1 þ cos(2vt þ 2FÞ ¼ 2 2 and hence s2Y ¼ s2X =2 ¼ 1.
and s2X ¼ CX (0) ¼ RX (0) ¼ 2
19.2
Stationary Random Processes
435
FIGURE 19.2.7
Autocorrelation: RY (t) ¼ E ½Y(t)Y(t þ t) ¼ E ½X(t) cos(vt þ F)X(t þ t) cos(vt þ vt þ F) 1 ¼ RX (t) E ½cos(vt) þ cos(2vt þ vt þ 2F) 2 RX (t) RX (t) cos(vt) þ E ½cos(2vt þ vt þ 2F) ¼ 2 2 From Example 19.2.1 E ½cos(2vt þ vt þ 2F) ¼ 0, and hence RY (t) ¼
RX (t) cos(vt) ¼ e2ljtj cos(vt) 2
A graph of RY(t) is shown in Fig. 19.2.7 with l ¼ 0.5 and v ¼ 2p. Example 19.2.9 (Cross-Correlation) Two random processes X(t) and Y(t) are given by X(t) ¼ A cos(vt) þ B sin(vt) and Y(t) ¼ A sin(vt) þ B cos(vt), where A and B are random variables with density functions fA(a) and fB(b). We have to find the cross-correlation, cross-covariance, and the normalized cross-covariance between X(t) and Y(t). From Example 19.2.4 the processes X(t) and Y(t) are stationary if E [A] ¼ E [B] ¼ 0, E [A 2] ¼ E [B 2], and E [AB] ¼ 0. Hence the mean values mX ¼ mY ¼ 0. The variances of these processes are E ½A2 ¼ E ½B2 ¼ s2 . Cross-Correlation. The cross-correlation function RXY(t1, t2) can be written as RXY (t1 , t2 ) ¼ E ½X(t1 )Y(t2 ) ¼ E{½A cos(vt1 ) þ B sin(vt1 )½A sin(vt2 ) þ B cos(vt2 )} ¼ E{ A2 cos(vt1 ) sin(vt2 ) þ B2 sin(vt1 ) cos(vt2 ) þ AB½cos(vt1 ) cos(vt2 ) sin(vt1 ) sin(vt2 )}
436
Random Processes
Substituting the values E [A 2] ¼ E [B 2] ¼ s2 and E [AB]¼0 in this equation, we obtain RXY (t1 ,t2 ) ¼ s2 sin½v(t1 t2 ) ¼ s2 sin½vt where we have substituted t ¼ (t2 2 t1). The term RXY(t) is shown in Fig. 19.2.8 for s2¼4 and v ¼ 2p. Here RXY(t) is an odd function, unlike the autocorrelation function. Since RXY (t)¼0 for t ¼ 0, we conclude that X(t) and Y(t) are orthogonal. Cross-Covariance. Since mX ¼ mY ¼ 0, CXY (t) ¼ RXY(t). Normalized Cross-Covariance:
rXY (t) ¼
CXY (t) s2 sin½vt ¼ ¼ sin½vt s2 s2
Example 19.2.10 A random telegraph wave X(t) as in Example 19.2.7 is passed through a linear system with impulse response h(t) ¼ e – btu(t), where u(t) is a unit step function. The output of the system is Y(t). It is desired to find the cross-correlation function RXY (t). From Example 19.2.7 the autocorrelation function RX (t) ¼ 14 {1 þ e2ljtj }. Hence, from Eq. (19.2.23), we have ð1
1 (1 þ e2ljtaj )eba da 4 ð ð 8 1 1 t 2l(ta) ba 1 1 2l(at) ba > > þ e e da þ (e )e da < 4b 4 0 4 t ð1 ¼ > 1 1 > : þ e2l(at) eba da 4b 4 0
RXY (t) ¼
0
FIGURE 19.2.8
t.0 t0
19.2
Stationary Random Processes
437
Evaluating the integrals and substituting l ¼ 1 and b ¼ 0.5, we obtain 8 lebt e2lt > > ; t.0 > > 2 2 < 4l b 4(2l b) 1 þ RXY (t) ¼ 4b > > e2lt > > : , t0 4(2l þ b) or
8 4 (t=2) 1 2t > > e e , > < 15 6
1 RXY (t) ¼ þ 2 > > > : 1 e2t , 10
t.0
t0
The cross-correlation function RXY (t) is shown in Fig. 19.2.9. RXY (t) does not possess any symmetry, unlike RX (t). Moments of Discrete-Time Stationary Processes
In actual practice observations are made on the sample function of a stationary random process X(t) at equally spaced time intervals fti, i ¼ 0, +1, . . .gwith corresponding sequence of random variables fXi, i ¼ 0, +1, . . .g. These observation random variables will not be independent. Since fXig are samples of a stationary random process, the means and variances of these samples are the same as in the original process: E ½Xi ¼ mX ; var½Xi ¼ s2X ,
i ¼ 0, +1, . . .
(19:2:32)
Analogous to the continuous case, we can define the various second moments: Autocovariance: CX (h) ¼ E ½(Xi mX )(Xiþh mX ),
i ¼ 0,+1, . . .
(19:2:33)
Autocorrelation: RX (h) ¼ E ½Xi Xiþh , i ¼ 0, + 1, . . .
FIGURE 19.2.9
(19:2:34)
438
Random Processes
Normalized Autocovariance (NACF): rX (h) ¼
CX (h) CX (h) ¼ CX (0) s2X
(19:2:35)
If fYi, i ¼ 0, +1, . . .g is the sequence obtained from a second stationary random process Y(t) with mean mY and variance s2Y, then we can define the cross-moments as follows: Cross-Covariance: CXY (h) ¼ E ½(Xi mX )(Yiþh mY i ¼ 0,+1, . . .
(19:2:36)
Cross-Correlation: RXY (h) ¼ E ½Xi Yiþh i ¼ 0,+1, . . .
(19:2:37)
Normalized Cross-Covariance (NCCF): CXY (h) CXY (h) rXY (h) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ sX sY CX (0)CY (0)
Example 19.2.11
(19:2:38)
A discrete zero mean stationary random process Xi is given by Xi ¼ fXi1 þ vi ,
i ¼ 1, . . .
(19:2:39)
where vi is a zero mean Gaussian random process with variance s2v . We want to find the variance of Xi and the NACF rX(h). Multiplying both sides of Eq. (19.2.39) by Xi and taking expectations, we have E ½Xi2 ¼ fE ½Xi Xi1 þ E ½Xi vi or
s2X ¼ fCX (1) þ E ½Xi vi
(19:2:40)
The cross-correlation E ½Xi vi can be computed as follows: E ½Xi vi ¼ E ½(fXi1 þ vi )vi ¼ s2v
(19:2:41)
since vi occurs after Xi21. Hence, substituting Eq. (19.2.41) in Eq. (19.2.40) and dividing throughout by s2X , we obtain 1 ¼ frX (1) þ
s2v s2X
and s2X ¼
s2v 1 frX (1)
(19:2:42)
Premultiplying both sides of Eq. (19.2.39) by Xih and taking expectations, we have E ½Xih Xi ¼ E ½fXih Xi1 þ E ½Xih vi
(19:2:43)
In Eq. (19.2.43) E ½Xih vi ¼ 0 since vi occurs after Xih for h . 0; hence CX (h) ¼ fCX (h 1),
h.0
(19:2:44)
Dividing Eq. (19.2.44) by CX (0) ¼ s2X , we obtain an equation for the NACF rX (h) rX (h) ¼ frX (h 1),
h.0
(19:2:45)
19.3
Ergodic Processes
439
and solving for rX(h) with initial condition rX(0) ¼ 1, we obtain rX (h) ¼ fh ,
h.0
(19:2:46)
Substituting Eq. (19.2.46) in Eq. (19.2.42), we have s2X ¼
s2v 1 f2
(19:2:47)
The process defined by Eq. (19.2.39) is called an autoregressive process of order 1.
B 19.3
ERGODIC PROCESSES
The ensemble average of a random process X(t) is the mean value mX(t) defined by, ð1 mX (t) ¼ xfX (x; t)dx (19:3:1) 1
Finding the ensemble average mX(t) requires storing a multiplicity of sample functions and finding the average. In many instances this process may be nontrivial. Given a sample function of any random process X(t), the time average m^ X is defined by m^ X ¼
1 2T
ðT X(t)dt
(19:3:2)
T
A reasonable question to ask is whether the ensemble average can be obtained from a much easier time average. We observe that the ensemble average is not a random variable but is a function of time, whereas the time average is a random variable that is not a function of time. If these averages are to be equal, then the first condition that we have to impose is that the random process X(t) be stationary, which removes the time factor in the mean value. Under these conditions, we can write E
1 2T
ð 1 T X(t)dt ¼ E ½m^ X ¼ E ½X(t)dt ¼ mX 2T T T
ðT
(19:3:3)
and m^ X is an unbiased estimator of mX. If a random variable is to be equal to a constant, then the second condition from Example 14.1.1 is that the variance of m^ X must tend to zero as T ! 1. Such random processes are said to satisfy the ergodic hypothesis. We will now derive the conditions for a random process to be mean-ergodic and correlation-ergodic. Mean-Ergodic
A stationary random process X(t) is called mean-ergodic if the ensemble average is equal to the time average of the sample function x(t). We will assume that the following conditions are satisfied by X(t): X(t) is stationary, implying that it has a constant mean mX, and the autocorrelation function RX (t, t þ t) is a function of t only, or RX (t, t þ t) ¼ RX (t). RX (0) ¼ E [X 2(t)] is bounded, or RX(0) ,1. Hence CX (0) ¼ RX (0) 2 m2X ,1.
440
Random Processes
We have to find the conditions under which the variance s2m^ X of the time average m^ X goes to 0. The variance s2m^ X is given by s2m^ x ¼ E ½(m^ X mX )2 ¼ E
(
1 2T
2 )
ðT ½X(t) mX dt T
(
) ð ð 1 2 T T ¼E ½X(t) mX ½X(s) mX dt ds 2T T T 2 ð T ð T 1 ¼ CX (t,s)dt ds 2T T T 2 ð T ð T 1 ¼ CX (s,t)dt ds, X(t) is stationary 2T T T
ð19:3:4Þ
Substitution of t ¼ s 2t and u ¼ t in Eq. (19.3.4), transforms the coordinate system from [s,t] to [t,u]. The Jacobian kJk of the transformation is given by 3
@t
1 @s 7
¼ 5 @s 0
@s
2
@t
6 kJk ¼ 4 @t
@s
@t
1
¼1 1
(19:3:5)
Thus s2m^ X ¼
1 2T
2 ð ð CX (t)dt du
(19:3:6)
The integration has to be carried out over the parallelogram shown in Fig. 19.3.1 in regions I and II. Region I: t 0: Limits for t are from – 2T to 0; limits for u are from – T to T þ t. Region II: t . 0: Limits for t are from 0 to 2T; limits for u are from – T þ t to T.
FIGURE 19.3.1
19.3
Ergodic Processes
Substituting these limits in Eq. (19.3.6), we obtain the variance s2m^ X : 2 ð T ð 2T ð Tþt ð 0 1 2 sm^ X ¼ CX (t)dt du þ CX (t)dt du 2T Tþt 0 T 2T ð 2T ð0 1 CX (t)½2T tdt þ CX (t)½2T þ tdt ¼ 2 4T 0 2T ð 2T ð 2T 1 1 jtj ¼ 2 CX (t)½2T jtjdt ¼ CX (t) 1 dt 4T 2T 2T 2T 2T
441
(19:3:7)
Finally, if the random process is to be mean-ergodic, the following condition has to be satisfied: ð 2T 1 jtj 2 CX (t) 1 lim s ¼ lim dt ! 0 (19:3:8) T!1 m^ X T!1 2T 2T 2T Since
ð 2T ð 2T
ð 2T
jtj jtj
dt C (t) 1 jC (t)j 1 jCX (t)jdt dt X X
2T 2T
2T
2T
(19:3:9)
2T
Ð1 hence, if 1 jCX (t)jdt is bounded, then limT!1 s2m^ X does indeed go to zero. A necessary and sufficient condition for a random process to be mean-ergodic is ð1 jCX (t)jdt , 1 (19:3:10) 1
Even though we have derived necessary and sufficient condition for a mean-ergodic process, we cannot use it to test the ergodicity of any process since it involves prior knowledge of the autocovariance function CX(t). However, if a partial knowledge of the ACF such as jCX(t)j goes to 0 as t ! 1, then we can conclude that the process is ergodic. Although ergodic processes must be stationary, stationary processes need not be ergodic. Since it is rather difficult to show that any arbitrary random process is ergodic, we will assume, unless otherwise stated, that a process that is stationary is also ergodic. Correlation-Ergodic
The time autocorrelation function of a stationary random process X(t) is defined by ð 1 T R^ X (l) ¼ X(t)X(t þ l)dt (19:3:11) 2T T A stationary random process X(t) is correlation-ergodic if ðT ð 1 T ^ 1 RX (l)dt ¼ lim lim X(t)X(t þ l)dt ¼ RX (l) T!1 2T T T!1 2T T
(19:3:12)
for all l. Clearly R^ X (l) is a random variable, but ð T ð T 1 1 E E ½R^ X (l) ¼ X(t)X(t þ l)dt ¼ RX (l)dt ¼ RX (l) 2T 2T T T and R^ X (l) is an unbiased estimator of RX (l). As in the case of mean-ergodic processes, here also the variance of R^ X (l) must tend to 0 as T ! 1. Hence we can form the
442
Random Processes
variance s2R^ l of R^ X (l) for any fixed l: X ( ð 2 ) h i2 T 1 2 ½X(t)X(t þ l) RX (l)dt sR^ l ¼ E R^ X (l) RX (l) ¼E X 2T T 2 ð T ð T
1 ¼ E ½ X(t)X(t þ l) RX (l)½ X(s)X(s þ l) RX (l) dt ds 2T T T 2 ð T ð T 1 ¼ CRlX (t,s)dt ds 2T T T 2 ð T ð T 1 CRlX (s t)dt ds ¼ 2T T T
(19:3:13)
Substituting (s – t) ¼ t in Eq. (19.3.13), we obtain a result similar to Eq. (19.3.8), namely ð 2T 1 jtj lim s2R^ l ¼ lim CRlX (t) 1 dt ! 0 (19:3:14) T!1 T!1 2T X 2T 2T and the necessary and sufficient condition for X(t) to be correlation-ergodic is ð1
l
C (t) dt , 1 (19:3:15) RX 1
where CRlX (t) ¼ RRlX (t) R2X (l) RRlX ¼ E ½X(t)X(t þ l)X(t þ t)X(t þ t þ l) Thus the autocorrelation ergodicity involves the fourth-order stationary moments of X(t). As a corollary to this result, we can define mean-square or power ergodicity as ðT 1 2 lim X (t)dt ¼ RX (0) (19:3:16) T!1 2T T and the necessary and sufficient conditions for mean-square ergodicity are ð 2T 1 jtj lim s2R^ 0 ¼ lim CR0 X (t) 1 dt ! 0 T!1 T!1 2T X 2T 2T
(19:3:17)
or ð1 1
jCR0 X (t)jdt , 1
(19:3:18)
where CR0 X (t) ¼ R0RX (t) R2X (0) R0RX (t) ¼ E{½X(t)X(t þ t)2 } Example 19.3.1 We will revisit Example 19.2.1, where X(t) ¼ A sin(v0t þ F), where A and F are independent and A is uniformly distributed over (0,1) and F is uniformly distributed over (0,2p) and find whether it is mean- and power-ergodic.
19.3
Ergodic Processes
443
We have already seen in Example 19.2.1 that E ½X(t) ¼ E ½AE ½sin(v0 t þ F) ¼ 0 RX (t) ¼ E ½X(t)X(t þ t) ¼ E ½A1 E ½sin(v0 t þ F) sin(v0 t þ v0 t þ F) 1 ¼ E ½A2 cos(v0 t) 2 and X(t) is indeed stationary. In fact, it is also strict-sense stationary. We will check first whether it is mean-ergodic. Since the mean value is zero, CX (t) ¼ 12 E ½A2 cos(v0 t). The variance of the time average s2m^ X is given by s2m^ X
E ½A2 ¼ 2T
ð 2T jtj cos(v0 t) dt 1 2T 2 2T
We can use the result from Fourier transform that time multiplication is frequency convolution and evaluate the integral shown above. Substituting the Fourier transforms of the triangular and the cosine functions jtj sin(vT ) 2 cos(v0 t) p 1 () ½d(v þ v0 ) þ d (v v0 ) and () 2T 2T vT 2 2 in the equation for the variance, we obtain ð 1 E ½A2 1 sin(vT ) 2 p s2m^ X ¼ ½d(v þ v0 ) þ d(v v0 )dv 2T 2p 2T 1 vT 2 E ½A2 sin(v0 T ) 2 ¼ 2p v0 T 2 and as T ! 1, sm^ X ! 0, for v0 = 0. Thus X(t) is mean-ergodic. Substituting E ½A2 ¼ 13 in the variance equation, we obtain 1 sin(v0 T ) 2 2 sm^ X ¼ 6p v0 T
To check for power ergodicity, it is simpler to use Eq. (19.3.16) rather than Eq. (19.3.17). Thus
1 lim T!1 2T
ðT
1 X (t) dt ¼ lim T!1 2T T 2
ðT
2
2
A cos (v0 t þ F) dt ¼ T
A2 2
for
v0 = 0
Since this expression does not converge to RX (0) ¼ 16, the process X(t) is neither powerergodic nor correlation-ergodic. Example 19.3.2 A random process X(t) ¼ A, where A is random variable uniformly distributed over (0,1]. Since E ½A ¼ 12 and RA (t) ¼ E ½A2 ¼ 13, this process is stationary. To check for ergodicity in the mean, we apply Eq. (19.3.2) for the time average and check whether it tends to the ensemble average as T tends to 1. Thus ð ð 1 T 1 T m^ X ¼ X(t)dt ¼ A dt ¼ A as T ! 1 2T T 2T T
444
Random Processes
is not equal to the ensemble average mX ¼ 12. Hence X(t) is neither mean-ergodic nor correlation-ergodic. Example 19.3.3 We now consider a random process X(t) ¼ B þ A sin(v0t þ F), where B, A, and F are independent random variables. A and B are uniformly distributed over (0,1] and (0,2] respectively. We will check whether X(t) is mean-ergodic. We first determine whether X(t) is stationary. The mean value mX is given by E ½B þ A sin(v0 t þ F) ¼ mB þ E ½A sin(v0 t þ F) ¼ mB since E ½A sin(v0 t þ F) ¼ 0 from previous examples. The autocorrelation function is RX (t) ¼ E{½B þ A sin(v0 t þ F)½B þ A sin(v0 t þ v0 t þ F)} ¼ E ½B2 þ 0 þ 0 þ E ½A2 E ½sin(v0 t þ F)A sin(v0 t þ v0 t þ F 1 ¼ E ½B2 þ E ½A2 E ½cos(v0 t) cos(2v0 t þ v0 t þ 2F 2 1 ¼ E ½B2 þ E ½A2 cos(v0 t) 2 Hence the process X(t) is stationary. The autocovariance function CX(t) is given by 1 CX (t) ¼ RX (t) m2X ¼ E ½B2 þ E ½A2 cos(v0 t) m2B 2 1 ¼ s2B þ E ½A2 cos(v0 t) 2 and substituting this equation in Eq. (19.3.7), we obtain the variance of the time average as ð 2T 1 1 jtj s2B þ E ½A2 cos(v0 t) 1 dt s2m^ X ¼ 2T 2T 2 2T " 2 # 1 2T sin(v T ) E ½A2 sin(v0 T ) 2 0 2 2 2 2TsB þ E ½A ¼ sB þ ¼ 2T 2p v0 T 2p v0 t and ( lim s2 T!1 m^ X
¼
lim s2B T!1
) E ½A2 sin(v0 T ) 2 þ ¼ s2B 2p v0 T
if
v0 = 0
We find that even though X(t) is stationary, it is not mean-ergodic. Hence the time average is not a good estimator of the ensemble average. We can now substitute E ½A2 ¼ 13 and s2B ¼ 13 in the variance equation and write s2m^ X ¼ s2B þ
E ½A2 sin(v0 T ) 2 1 sin(v0 T ) ¼ 1þ 2p v0 T 3 2pv0 T
Example 19.3.4 The autocovariance of a zero mean Gaussian random process X(t) is given by CX (t) ¼ s2X e2ajtj , where s2X is its variance. We will check whether this
19.4
Estimation of Parameters of Random Processes
445
process is mean-ergodic and correlation-ergodic. The variance of the time average from Eq. (19.3.7) is s2m^ X
ð 2T jtj 1 jtj 2 2ajtj CX (t) 1 s e 1 dt ¼ dt 2T 2T 2T X 2T 2T s2X 1 e4aT 1 ¼ 2aT 4aT
1 ¼ 2T
ð 2T
and limT!1 s2m^ X ¼ 0. Hence the process is mean-ergodic. To determine whether it is correlation-ergodic, the fourth moment RRlX (t) ¼ E ½X(t)X(t þ l)X(t þ t)X(t þ t þ l) is calculated from the following result. If fX1,X2,X3,X4) are zero mean Gaussiandistributed random variables, then E ½X1 X2 X3 X4 ¼ E ½X1 X2 E ½X3 X4 þ E ½X1 X3 E ½X2 X4 þ E ½X1 X4 E ½X2 X3 . Hence E ½X(t)X(t þ l)X(t þ t)X(t þ t þ l) ¼ E ½X(t)X(t þ l)E ½X(t þ t)X(t þ t þ l) þ E ½X(t)X(t þ t)E ½X(t þ l)X(t þ t þ l) þ E ½X(t)X(t þ t þ l)E ½X(t þ l)X(t þ t) ¼ R2X (l) þ R2X (t) þ RX (t þ l)RX (t l) The covariance CRlX (t) ¼ RRlX (t) R2X(l) ¼ R2X (t) þ RX (t þ l)RX (t l), and substituting for the correlation functions, we obtain CRlX ðtÞ as CRlX (t) ¼ s4X e4ajtj þ s4X e2ajtþlj e2ajtlj ¼ 2s4X e4ajtj , t . l Substituting for CRlX (t) in Eq. (19.3.14), we obtain lim s2^ l T!1 RX
1 ¼ lim T!1 2T
ð 2T 2T
jtj 1 dt 2T
1 e8aT ¼ lim 1 !0 T!1 aT 8aT s4X
2s4X e4ajtj
A similar result holds good for t , l. We conclude from the preceding result that the process X(t) is also correlation-ergodic and hence power-ergodic.
B 19.4 ESTIMATION OF PARAMETERS OF RANDOM PROCESSES 19.4.1
Continuous-Time Processes
The ergodic hypothesis detailed in the previous section enables us to define estimators for the parameters of a random process from its sample realization. If X(t) is a sample realization of a stationary ergodic random process available only for a time duration T, then the estimators for the parameters, mean, variance, autocorrelation, autocovariance, and normalized autocovariance can be defined.
446
Random Processes
Mean: m^ X ¼
1 T
ðT X(t)dt
(19:4:1)
0
We note that m^ X is an unbiased estimator of mX since ðT ð 1 1 T X(t)dt ¼ E½X(t)dt ¼ mX E ½m^ X ¼ E T 0 T 0 The variance of m^ X is very similar to Eq. (19.3.7) and is given by ð 1 T jtj ¼ CX (t) 1 var½m^ X ¼ dt T T T ð h 2 T ti CX (t) 1 dt , even function ¼ T 0 T s2m^ X
(19:4:2)
Variance: s^ 2X
1 ¼ T
ðT
½X(t) m^ X 2 dt
(19:4:3)
0
If the mean value mX is known, then s^ 2X will be an unbiased estimator. We will determine the conditions under which s^ 2X given by Eq. (19.4.3) can be an unbiased estimator: ðT 1 ½X(t) m^ X 2 dt T 0 ðT 1 ^ mX ) (m^ X mX )2 dt ¼E ½X(t) T 0 ( ð " # ) 1 T (X(t) mX )2 þ (m^ X mX )2 ¼E dt T 0 2(X(t) mX )(m^ X mX ) ( ð )
1 T 2 ¼E ½(X(t) mX ) dt þ E (m^ X mX )2 T 0 s2 s2
E ½s^ 2X ¼ E
X
2 E T2
m^ X
ð T ð T
½X(t) mX ½X(s) mX dsdt
0
0
and from Eq. (19.4.2) we have E ½s^ 2X ¼ s2X þ s2m^ X
2 T
ð T
jtj CX (t) 1 dt T T s2m^
¼
s2X
s2m^ X
X
(19:4:4)
Hence s^ 2X cannot be an unbiased estimator for finite T with the bias equal to s2m^ X . However, it is asymptotically unbiased.
19.4
Estimation of Parameters of Random Processes
447
Autocovariance. Two estimators for the autocovariance function are given by ð Tt 1 C X (t) ¼ ½X(t) m^ X ½X(t þ t) m^ X dt, 0 , t T (19:4:5) T t 0 ð 1 Tt ½X(t) m^ X ½X(t þ t) m^ X dt, 0 , t T (19:4:6) C^ X (t) ¼ T 0 If the mean mX is known, then CX (t) given by Eq. (19.4.5) is an unbiased estimator since ð Tt 1 E ½CX (t) ¼ E{½X(t) mX ½X(t þ t) mX } dt T t 0 ¼ CX (t)
T t ¼ CX (t), 0 , t T T t
and C^ X (t) is a biased estimator as shown below: ð 1 Tt ^ ½X(t) mX ½X(t þ t) mX dt CX (t) ¼ T 0 h T t ti ¼ CX (t) 1 , 0 , t T ¼ CX (t) T T with bias equal to CX (t)(t=T ). In actual practice only the sample mean m^ X is known, and we will determine whether C X (t) can be an unbiased estimator. Equation (19.4.5) can be rewritten as ð Tt1 1 E ½C X (t1 ) ¼ E ½X(t) m^ X ½X(t þ t1 ) m^ X dt T t1 0
1 ¼E T t1
ð Tt1
½(X(t) mX ) ðm^ X mX )
0
½(X(t þ t1 ) mX ) (m^ X mX )dt
,
0 , t1 T
ÐT and substituting for m^ X ¼ (1=T ) 0 X(s)ds in the equation above, we have ð Tt1 1 2 ½(X(t) mX )(X(t þ t1 ) mX ) þ (m^ X mX ) dt E ½CX (t1 ) ¼ E T t1 0 ð Tt1 1 E ½(X(t) mX )(m^ X mX ) T t1 0 þ ½(X(t þ t1 ) mX )(m^ X mX )dt ð Tt1 ð T 1 ¼ CX (t1 ) þ s2m^ X CX (s t)ds dt T(T t1 ) 0 0 ð Tt1 ð T 1 CX (s t t1 )ds dt, 0 , t1 T T(T t1 ) 0 0 where E ½(m^ X mX )2 ¼ s2m^ X ¼
1 T
jtj CX (t) 1 dt [from Eq. (19:4:2) T T
ðT
(19:4:7)
448
Random Processes
FIGURE 19.4.1
In Eq. (19.4.7) we make the transformation t ¼ (s – t) and u ¼ t. As the Jacobian of the transformation is 1, we can use techniques similar to those employed in Eqs. (19.3.5)– (19.3.7) to determine E ½C X (t1 ). This transformation amounts to finding the integral in the parallelogram shown in Fig. 19.4.1. The integration along the t axis is from – T to (T – t1). Thus ð ð Tt1 1 E ½CX (t1 ) ¼ CX (t1 ) þ s2m^ X du ½CX (t) þ CX (t t1 )dt (19:4:8) T(T t1 ) T Ð The limits of integration for du are determined as follows in the three regions: Region I: 2T , t 2 t1 jtj du ¼ T þ t ¼ T 1 T
(19:4:9a)
t1 du ¼ T t1 ¼ T 1 T
(19:4:9b)
t þ t1 du ¼ T t t1 ¼ T 1 T
(19:4:9c)
ð tþT=2 T=2
Region II: 2t1 , t 0 ð T=2t1 T=2
Region III: 0 , t (T 2 t1) ð T=2t1 tT=2
Substituting Eqs. (19.4.9) in Eq. (19.4.8), we obtain E ½C X (t1 ) as follows: E½CX (t1 ) ¼ CX (t1 ) þ s2m^ X 9 8 ð t1 jtj > > > > 1 (t) þ C (t t )dt ½C X X 1 > > > > T > > T > > > > ð = < i 0 h 1 t1 , 0 , t1 T 1 ½CX (t) þ CX (t t1 )dt þ > (T t1 ) > T t1 > > > > ð > > Tt1 h > > > > t þ t1 i > ; : þ 1 ½CX (t) þ CX (t t1 )dt > T 0 (19:4:10)
19.4
Estimation of Parameters of Random Processes
449
For large values of T or for low values of the lag t1, the terms within braces can be approximated as 2s2m^ X , and the approximation for E ½CX (t1 ) is E ½C X (t1 ) CX (t1 ) s2m^ X ,
0 , t1 T
(19:4:11)
Thus, CX (t) is a biased estimator with the bias B s2m^ X . In an analogous manner the expected value of C^ X (t1 ) given by Eq. (19.4.6) can be evaluated as follows: ð Tt1 1 ^ E ½CX (t1 ) ¼ E ½(X(t) mX )(X(t þ t) mX ) þ E(m^ X mX )2 dtg T 0 ð Tt1 1 E ½(X(t) mX )(m^ X mX ) þ ½(X(t þ t1 ) mX )(m^ X mX )dt T 0 ð ð T t1 1 Tt1 T ½CX (t1 ) þ s2m^ X 2 ¼ CX (s t)ds dt T 0 T 0 ð ð 1 Tt1 T CX (s t t1 )ds dt, 0 , t1 T 2 T 0 0 (19:4:12) By substituting t ¼ (s – t) and u ¼ t and performing the integration in the parallelogram shown in Fig. 19.4.1, the final result similar to Eq. (19.4.10) is T t1 E ½C^ X (t1 ) ¼ ½CX (t1 ) þ s2m^ X T 9 8 ð t1 jtj > > > > 1 (t) þ C (t t ) dt ½C X X 1 > > > > T > > T > > > > ð = < i 0 h 1 t1 , 0 , t1 T 1 þ ½CX (t) þ CX (t t1 ) dt > T> T t1 > > > > ð Tt1 h > > > > > > t þ t1 i > ; : þ 1 ½CX (t) þ CX (t t1 ) dt > T 0 (19:4:13) For large values of T or for low values of the lag t1, the terms within braces can be approximated as ½(T t1 )=T2s2m^ X , and the approximation for E ½C^ X (t1 ) is h t1 i E ½C^ X (t1 ) ½CX (t1 ) s2m^ X 1 (19:4:14) , 0 , t1 T T Thus, C^ X (t1 ) is also a biased estimator with a larger bias than C X (t) with the bias h t1 t1 i B CX (t1 ) s2m^ X 1 , 0 , t1 T T T We will show later that C^ X (t) is the preferred estimator as it gives better minimum meansquare error. Example 19.4.1 The autocovariance function of a random process X(t) is CX (t) ¼ eajtj , with a ¼ 0.5. The data interval T ¼ 50. We will find the variance of the estimated mean
450
Random Processes
value m^ X as a function of T, compare the true value CX(t) to E½CX ðtÞ given by Eq. (19.4.10) and E ½C^ X (t) given by Eq. (19.4.13), and finally compare the approximations to E ½C^ X (t) given by Eq. (19.4.14). Variance s2m^ X (T ): From Eq. (19.4.2), we obtain
s2m^ X (T )
2 ¼ T ¼
ð T
2½e
ð h ti 2 T at h ti CX (t) 1 dt ¼ e 1 dt T T 0 T
0 aT
þ aT 1 a2 T 2
A graph of s2m^ X (T ) is shown in Fig. 19.4.2 for a ¼ 0.5, and the estimate of the mean m^ X is asymptotically unbiased since limT!1 s2m^ X (T ) ! 0. Means of Estimated Autocovariances. C X (t) and C^ X (t). From Eq. (19.4.10) the mean value of C X (t) is given by 2 e50a þ 50a 1 E CX (t) ¼ e þ 2500a2 ðt ab 1 jbj e þ ea(bt) 1 db 50 t 50 50 ð0 ab 1 t e þ ea(bt) 1 db 50 t t 50 ð 50t ab 1 bþt e þ ea(bt) 1 db, 50 t 0 50
at
FIGURE 19.4.2
0 , t 50
19.4
Estimation of Parameters of Random Processes
451
and from Eq. (19.4.13) the mean value of C^ X (t) is given by h i 50 t 2 e50a þ 50a 1 at ^ e E CX (t) ¼ þ 2500a2 50 ðt ab 1 jbj a(bt) e þe 1 db 50 50 50 ð 1 0 ab t e þ ea(bt) 1 db 50 t 50 ð 1 50t ab bþt e þ ea(bt) 1 db, 50 0 50
0 , t 50
In graphing the estimated autocovariance functions, it is usual to plot the lag length t upto 10– 20% of the length T. Expected values E ½C X (t) and E ½C^ X (t) are shown along with CX (t) in Fig. 19.4.3 for t ¼ 0– 10 and tabulated in Table 19.4.1. The bias as shown in the figure is approximately equal to B s^ 2mX (T ) ¼ 0:0768.
FIGURE 19.4.3
TABLE 19.4.1. t
CX (t)
E[C X (t)]
CX (t)
ˆ X (t)] E[C
CˆX (t)]
0 1 2 3 4 5 6 7 8 9 10
1 0.606531 0.367879 0.22313 0.135335 0.082085 0.049787 0.030197 0.018316 0.011109 0.006738
0.9232 0.529171 0.291129 0.147612 0.061327 0.009859 20.020499 20.038005 20.047664 20.052531 20.054435
0.9232 0.529731 0.291079 0.14633 0.058535 0.005285 20.027013 20.046603 20.058484 20.065691 20.070062
0.9232 0.518588 0.279484 0.138755 0.056421 0.008873 20.018039 20.032684 20.040038 20.044075 20.043548
0.9232 0.519136 0.279436 0.13755 0.053852 0.004756 20.023771 20.040078 20.049127 20.053867 20.05605
452
Random Processes
The figure also shows that there is very little difference between E ½C X (t) and E ½C^ X (t). Table 19.4.1 also shows the approximation E ½C X (t) given by Eq. (19.4.11) and the approximation E ½C^ X (t) given by Eq. (19.4.14). Covariance of ACF Estimators. Before finding the covariance of the estimator C X (t) at two different lags t1 and t2 , we will first find the covariance cov ½X(t)X(tþt1 ), X(t)X(s þ t2 ): cov½X(t)X(t þ t1 ), X(s)X(s þ t2 ) ¼ E{½(X(t) mX )(X(t þ t1 ) mX )½(X(s) mX )(X(s þ t2 ) mX )} ¼ E ½(X(t) mX )(X(t þ t1 ) mX )(X(s) mX )(X(s þ t2 ) mX ) E ½(X(t) mX )(X(t þ t1 ) mX )E ½(X(s) mX )(X(s þ t2 ) mX ) (19:4:15) Expressing E ½(X(t) mX )(X(t þ t1 ) mX )(X(s) mX )(X(s þ t2 ) mX ) of Eq. (19.4.15) in terms of the fourth cumulant k4 from Eq. (11.6.8), we obtain E ½(X(t) mX )(X(t þ t1 ) mX )(X(s) mX )(X(s þ t2 ) mX ) ¼ k4 (s t, t1 , t2 ) þ CX (t1 )CX (t2 ) þ CX (s t)CX (s t þ t2 t1 ) þ CX (s t þ t2 )CX (s t t1 ) and substituting in Eq. (19.4.15), we obtain cov½X(t)X(t þ t1 ), X(s)X(s þ t2 ) ¼ k4 (s t, t1 , t2 ) þ CX (s t)CX (s t þ t2 t2 ) þ CX (s t þ t2 )CX (s t t1 ) (19:4:16) which is an exact expression. The presence of the fourth cumulant k4 in Eq. (19.4.16) renders evaluation of the covariance intractable. However, if X(t) is a Gaussian random process, then the fourth cumulant k4 is zero and the covariance of [X(t)X(t þ t1), X(s)X(s þ t2)] becomes cov½X(t)X(t þ t1 ), X(s)X(s þ t2 ) ¼ CX (s t)CX (s t þ t2 t2 ) þ CX (s t þ t2 )CX (s t t1 )
(19:4:17)
Assuming without loss of generality that 0 t1 t2, the covariance of the estimator C X (t) at two different lags t1 and t2 can be given by cov½C X (t1 ), CX (t2 ) ð Tt1 ð Tt2 1 1 ¼ cov X(t)X(t þ t1 ) dt X(s)X(s þ t2 ) ds (T t1 ) 0 (T t2 ) 0 ð Tt2 ð Tt1 1 ¼ cov½X(t)X(t þ t1 )X(s)X(s þ t2 )dt ds (T t1 )(T t2 ) 0 0 (19:4:18)
19.4
Estimation of Parameters of Random Processes
453
FIGURE 19.4.4
Substituting for the covariance in Eq. (19.4.18) from Eq. (19.4.16), we obtain cov½CX (t1 ), C X (t2 ) ¼
1 (T t1 )(T t2 )
ð Tt2 ð Tt1 0
0
CX (s t)CX (s t þ t2 t1 ) þ CX (s t þ t2 )CX (s t t1 )dt ds (19:4:19)
We now make the transformation t ¼ (s 2 t) and u ¼ t, and as the Jacobian of the transformation is 1, we can use techniques similar to those employed in Eq. (19.4.8) to determine the covariance. This transformation amounts to finding the integral in the parallelogram shown in Fig. 19.4.4. The integration along the t axis is from 2(T 2 t2) to (T 2 t1). Thus, for a Gaussian process X(t), we obtain cov½CX (t1 ), CX (t2 ) ð ð Tt1 CX (t)CX (t þ t2 t1 ) 1 du ¼ (T t1 )(T t2 ) (Tt2 ) þ CX (t þ t2 )CX (t t1 )d t (19:4:20) Ð The integral du in Eq. (19.4.20) is calculated in three regions as shown in Fig. 19.4.4 in a manner similar to that used in Eqs. (19.4.9): Region I: 2(T 2 t2) , t 0 ð tþT=2t2 T=2
jtj t2 du ¼ T þ t t2 ¼ T 1 T
(19:4:21a)
Region II: 0 (t2 2 t1) ð tþT=2t2 tT=2
t2 du ¼ T t2 ¼ T 1 T
(19:4:21b)
454
Random Processes
Region III: (t2 2 t1) , t (T 2 t1) ð T=2t1 tT=2
t þ t1 du ¼ T t t1 ¼ T 1 T
(19:4:21c)
Substituting Eqs. (19.4.21) into Eq. (19.4.20), the covariance between adjacent values t1 and t2 of the sample autocovariance for a Gaussian process X(t) is given by 1 (T t1 )(T t2 ) 8 ð0 (T jtj t2 )½CX (t)CX (t þ t2 t1 ) > > > > > þCX (t þ t2 )CX (t t1 )dt > > > Tt ð t22 t1 < (T t2 )½CX (t)CX (t þ t2 t1 ) þ > þCX (t þ t2 )CX (t t1 )dt 0 > > ð Tt1 > > (T t t1 )½CX (t)CX (t þ t2 t1 ) > > > :þ þCX (t þ t2 )CX (t t1 )dt t2 t1
cov½CX (t1 ), C X (t2 ) ¼
9 > > > > > > > > = > > > > > > > > ;
(19:4:22)
Although Eq. (19.4.22) was derived with the condition 0 t1 t2, it can be shown that it holds good for all t1 and t2. The covariance Ð is generally difficult to evaluate. For large T, (T 2 t1) T (T 2 t2), and the integral du T in Eq. (19.4.21). With these approximations the distinction between CX (t) and C^ X (t) becomes moot and Eq. (19.4.22) becomes cov½CX (t1 ), C X (t2 ) cov½C^ X (t1 ) C^ X (t2 ) ð 1 1 ½CX (t)CX (t þ t2 t1 ) þ CX (t þ t2 )CX (t t1 )dt T 1
(19:4:23)
The variance of the sample autocovariance function is obtained by substituting t1 ¼ t2 ¼Ð s in Eqs. (19.4.20) and (19.4.22). With this substitution Eqs. (19.4.21) become du ¼ T 2 s 2 jtj, and the general form for the variance is var½C X (s) ¼ ð Ts
1 (T jsj)2
(T s jtj)½CX2 (t) þ CX (t þ s)CX (t s) dt,
s,T
(19:4:24)
(Ts)
and the corresponding variance for the estimator C^ X (t) is obtained by substituting T 2 for (T jsj)2 in the denominator of Eq. (19.4.24). The estimator C^ X (t) is more often used since it yields a minimum mean-square estimator as shown in Example 19.4.2. The approximations for the variances CX (s) and C^ X (t) corresponding to Eq. (19.4.16) for large T are ð 1 1 ^ var½CX (s) var½CX (s) ½C 2 (t) þ CX (t þ s)CX (t s)dt (19:4:25) T 1 X Example 19.4.2 We will continue Example 19.4.1 assuming that X(t) is a zero mean Gaussian random process with autocovariance function CX(t) ¼ e – ajtj. Since we are
19.4
Estimation of Parameters of Random Processes
455
given that X(t) is zero mean, C X(t) will be an unbiased estimator. We have to find the ˆ X(t) and calculate the minimum meanvariances of the two estimators C X(t) and C – ajtj square error for both. Substituting CX(t) ¼ e in Eq. (19.4.24), the variance of C X(t) is given by
var CX (s) ¼
1 ðT jsjÞ2
ð Ts
(T s jtj) e2ajtj þ eajtþsj eajtsj dt
(19:4:26)
(Ts)
Since the integrand is even, Eq. (19.4.26) can be rewritten as
var C X (s) ¼
2 ð T sÞ 2
ð Ts
(T s t) e2at þ eajtþsj eajtsj dt
(19:4:27)
0
where we have removed the absolute sign from the quantities where it is clear that t is always positive. Equation (19.4.27) can integrated numerically to obtain the variance of the unbiased estimator. However, it is instructive to find a closed-form solution. The absolute value signs in the product term e – ajtþsj e – ajt2sj has to be carefully removed. In Fig. 19.4.5a, s , T/2 and the range of integration for t contains two segments (0,s) and (s, T 2 s). In Fig. 19.4.5b, s . T/2 and the range of integration for t contains only one segment (0, T – s). Hence we can write
0,s e
T 2
ajtþsj ajtsj
e
(
ea(tþs) ea(ts) ¼ e2as ,
0,ts
ea(tþs) ea(ts) ¼ e2at ,
s,tT s
¼
T ,sT 2 eajtþsj eajtsj ¼ ea(tþs) ea(ts) ¼ e2as ,
0,tT s
(19:4:28)
Substituting Eq. (19.4.28) in Eq. (19.4.27), the variance of the unbiased estimator CˆX(s) can written as follows: 9 8 ðs
2at > > 2as > > (T s t) e þe dt > > > > > > 0 > > > > ð < Ts T= 2 2at 2at þ (T s t) e þ e dt , 0 , s (19:4:29) var C X (s) ¼ 2> ð T sÞ 2 > s > > > > ð Ts > > > >
T > > > ; : ,sT> (T s t) e2at þ e2ats dt, 2 0
456
Random Processes
FIGURE 19.4.5
Integrating Eq. (19.4.29), we obtain the variance of the estimator CX(s) as var CX (s) 9 8 2a(Ts) e þ 2a(T s) 1 þ e2a(Ts) > > > > > > < þe2as 2a(T 2s) 1 þ 2a2 s(2T 3s), 0 , s T2 = 1 ¼ 2 > 2a2 ðT sÞ2 > > > > e2at(Ts) þ 2a(T s) 1 þ e2as T s , T , s T > ; : T 2 ð19:4:30Þ
The variance of the biased estimator CˆX(s) obtained by replacing (T 2 s)2 by T 2 in the denominator of Eq. (19.4.30). The minimum mean-square errors for both the estimators
19.4
Estimation of Parameters of Random Processes
457
FIGURE 19.4.6
are given by 2 1 2U (s) ¼ E C X (s) CX (s) ¼ var CX (s) h i2 h i 1 2 (s) ¼ E C^ X (s) CX (s) ¼ var C^ X (s) þ B2
(19:4:31)
B
ˆ X(s) is the same as its variance. The mean-square error for the unbiased estimator C ˆ X(s) as shown earlier for a zero mean process is B ¼ 2CX(t)fjtj/T], The bias B for C and substituting for B in Eq. (19.4.31), the mean-square error for the biased estimator ˆ X(s) is C h i t 2 1 2B (s) ¼ var C^ X (s) þ e2as T
(19:4:32)
The mean-square errors and the variances are shown in Fig. 19.4.6 for T ¼ 4, a ¼ 0.9. It is seen from the figure that the mean-square error for a biased estimator CˆX(s) is less than that of the unbiased one CˆX(s). Autocorrelation. If m ˆ X is zero in Eqs. (19.4.5) and (19.4.6), we obtain two corresponding estimators for the autocorrelation function RX(t): ð Tjtj 1 X(t)X(t þ t)dt, 0 t T (19:4:33a) Unbiased: RX (t) ¼ T jtj 0 ð 1 Tjtj ^ X(t)X(t þ t)dt (19:4:34a) Biased: RX (t) ¼ T 0 RX(t) is an unbiased estimator since ð Tjtj 1 X(t)X(t þ t)dt E RX (t) ¼ E T jtj 0 ð Tjtj 1 E½ X(t)X(t þ t)dt ¼ RX (t) ¼ T jtj 0
(19:4:33b)
458
Random Processes
and E [RˆX(t)] is a biased estimator as shown below: ð i T jtj Tjtj jtj ^ E RX (t) ¼ E X(t)X(t þ t)dt ¼ 1 RX (t) T T 0 h
(19:4:34b)
However, RˆX(t), although biased, gives a smaller mean-square error as shown in Example 19.4.2 and hence is the preferred estimator. Normalized Autocovariance: r^ X (t) ¼
C^ X (t) C^ X (0)
(19:4:35)
rˆX(t) will not be an unbiased estimator.
19.4.2 Discrete-Time Processes In actual practice observations are made on the sample function of a stationary ergodic random process X(t) at equally spaced time intervals t1, t2, . . . , tn with corresponding random variables X1, X2, . . . , Xn. These observation random variables will not be independent and will be used in the estimation of mean and autocovariance of the random process. Since fXig are samples of a stationary random process, the means and variances of these samples are the same as those of the original process: E ½Xi ¼ mX : var½Xi ¼ s2X , i ¼ 1, . . . ; n
(19:4:36)
Estimation of the Mean
In the previous chapter we estimated the mean m ˆ X of a random variable by minimizing its variance from a set of independent observation random variables fX1, . . . , Xng. We can use the same techniques to estimate the mean m ˆ X of the random process X(t) except that the set fX1, . . . , Xng is not independent. We define the estimated mean mˆX as the weighted sum of the dependent observations fX1, . . . , Xng: m^ X ¼
n X
ai Xi ¼ aT X ¼ XT a
(19:4:37)
i¼1
We have to determine the weight vector a if possible under the criteria of unbiasedness and minimum variance. The unbiasedness criterion yields the condition n X
ai ¼ aT 1 ¼ 1
(19:4:38)
i¼1
where 1 is a unity vector defined by 1 ¼ [1, . . . ,1]T. The variance of the estimator mˆX is given by var(m^ X ) ¼ s2m^ X ¼ E aT (X mX )(X mX )T a ¼ aT CX a
(19:4:39)
19.4
Estimation of Parameters of Random Processes
459
where CX is the autocovariance matrix of the vector random variable X, defined by CX ¼ E (X mX )(X mX )T 2 3 CX (1) CX (2) CX (n 1) CX (0) 6 CX (1) CX (0) CX (1) CX (n 2) 7 6 7 6 7 C (2) C (1) C (0) C (n 3) 6 7 ð19:4:40Þ X X X X ¼6 7 .. .. .. .. 6 7 4 5 . . . . CX ½(n 1) CX ½(n 2) CX ½(n 3) with CX(h) defined as the discrete autocovariance given by CX (h) ¼ E (Xi mX )(Xiþh mX
CX (0)
(19:4:41)
and CX(h) ¼ CX( –h) since CX is a real symmetric matrix. Equation (19.4.40) has to be minimized subject to the constraint of Eq. (19.4.38). The unconstrained function to be minimized after adjoining with the Lagrange multiplier l is given by J ¼ aT CX a þ l(aT 1 1)
(19:4:42)
T
where 1 is an n vector of ones given by 1 ¼ [1, 1, . . . , 1] . Differentiating with respect to the vector a, we obtain @J @ T ¼ a CX a þ l(aT 1 1) ¼ 2CX a þ l1 ¼ 0 @a @a Hence l a ¼ C1 1 2 X
(19:4:43)
Solving for l from the constraint equation [Eq. (19.4.38)] and substituting this result in Eq. (19.4.43), we obtain a¼
C1 X 1 1T C1 X 1
(19:4:44)
The solution for the coefficients fai, i ¼ 1, . . . , ng will not be the same for every n. If the Xi values are independent, then CX ¼ I and Eq. (19.4.44) reduces to 1 I 1 1 s2X a¼ 1¼ T 1¼ 1 1 n 1 1 1T 2 I:1 sX
(19:4:45)
and all coefficients have the same weight equaling 1/n, a result already obtained in Eq. (18.3.9). As it is, each ai in Eq. (19.4.44) will be different and the solution becomes cumbersome. To make the solution intuitively appealing we will assume that all the coefficients faig have the same weight ¼ 1/n and the estimator is given by n 1X m^ X ¼ Xi (19:4:46) n i¼1 The estimator given by Eq. (19.4.46) will be unbiased but will not be minimum variance. The variance of the estimator m ˆ X can be obtained from Eq. (19.4.39) 1 (19:4:47) var(m^ X ) ¼ s2mX ¼ 2 1T CX 1 n
460
Random Processes
or, in expanded form var(m^ X ) ¼
s2mX
" # n1 X 1 h CX (0) þ 2 ¼ 1 CX (h) n n h¼1
(19:4:48)
The variance given by Eq. (19.4.48) is not minimum. For independent Xis, CX(h) ¼ 0, h = 0, and the variance s2m^ X , given by s2m^ X ¼
1 T nCX (0) s2X 1 CX ¼ ¼ 2 n n2 n
is a minimum, a result that has been derived previously [in Eq. (18.3.11)].
Example 19.4.3 The autocovariance function of a random process X(t) is CX (h) ¼ eajhj , with a ¼ 0.5 as in Example 19.4.1. The number of data points n ¼ 50. We will find the variance of the estimated value m^ x . From Eq. (19.4.48) we can write the following equation for the estimated variance: " # n1 X 1 h 1þ2 var(m^ X ) ¼ s2m^ X (n) ¼ 1 eah n n h¼1 A closed-form solution for the summation given above can be obtained as a
s2m^ X (n) ¼
1 (n þ 2)e2a þ (n 2)e ne3a 2ea(n2) þ 2ea(n1) n n2 (1 ea )(1 2ea þ e2a )
and substituting a ¼ 0.5 in this equation, we obtain s2m^ X (n) ¼
pffiffiffi 1 ½7:3258e(n=2) (e e) þ 4:083n 7:83541 n2
and this is graphed in Fig. 19.4.7 along with the value CX(0)/n as the variance of the estimated mean for independent observations. The variance for n ¼ 50 is s2m^ X (50) ¼ 0:0785. We can see from the figure that the estimated mean m^ X is asymptotically unbiased since the variance s2m^ X (n) ! 0 as n ! 1.
FIGURE 19.4.7
19.4
Estimation of Parameters of Random Processes
461
Estimation of the Autocovariance
The autocovariance of a stationary ergodic random process X(t) has already been defined [Eq. (19.2.15)] as CX (h) ¼ E{½X(t) mX ½X(t þ h) mX }
(19:4:49)
where t has been replaced by h for convenience. The estimator CX (h) for the ACF from the observed random variables set {X1 , . . . , Xn } will be of the form CX (h) ¼
nh X
ai aiþh (Xi m^ X )(Xiþh m^ X )
(19:4:50)
i¼1
where m^ X is the unbiased estimator of the mean from Eq. (19.4.46). We have to choose, if possible, the coefficients {ai , i ¼ 1, . . . , n} such that E ½CX (h) ¼ CX (h). Equation (19.4.50) can be rewritten as follows: CX (h) ¼
nh X
ai aiþh ½(Xi mX ) (m^ X mX )½(Xiþh mX ) (m^ X mX )
i¼1
Expanding this equation and taking expectations, we obtain " # nh X (Xi mX )(Xiþh mX ) þ (m^ X mX )2 E ½CX (h) ¼ ai aiþh E (Xi mX )(m^ X mX ) (Xiþh mX )(m^ X mX ) i¼1 ¼
nh X
ai aiþh {CX (h) þ s2m^ X }
i¼1
" ¼
ai aiþh E ½(Xi mX )(m^ X mX )
i¼1
nh X
ai aiþh E(Xiþh mX )(m^ X mX )
i¼1 nh X
nh X
# ai aiþh (CX (h) þ
s2m^ X )
(S1 þ S2 )
(19:4:51)
i¼1
where the summations S1 and S2 have been defined as follows: S1 ¼
nh X
ai aiþh E ½(Xi mX )(m^ X mX )
i¼1
S2 ¼
nh X
(19:4:52) ai aiþh E ½(Xiþh mX )(m^ X mX )
i¼1
Substituting for m^ X from Eq. (19.4.46) in the summation S1 in Eq. (19.4.52), we can write S1 ¼
nh X n nh X n 1X 1X ai aiþh E ½(Xi mX )(X j mX ) ¼ ai aiþh CX ( j i) n i¼1 j¼1 n i¼1 j¼1
(19:4:53)
This equation represents the sum of every term of the (n 2 h) n matrix shown in Fig. 19.4.8.
462
Random Processes
FIGURE 19.4.8
The summation is carried out in three stages. The shaded parallelogram in region II consists of the sum of (n 2 h) (h þ 1) terms, and the shaded triangles in regions I and III consists of the remaining (n 2 h) (n 2 h 2 1) terms. Equation (19.4.53) can be rewritten in terms of these summations as follows:
S1 ¼
8 h X nh X > > < ai aiþh CX ( j) 1 n> > :
j¼0 i¼1
nh1 nh X X
þ
ai aiþh CX ( j)
j¼1 i¼jþ1
Region II
nh1 X nhj X
þ
j¼1
9 > > ai aiþh CX ( j þ h)=
i¼1
Region I
Region III
> > ; (19:4:54)
An equation similar to Eq. (19.4.53) for S2 is
S2 ¼
¼
nh X n 1X ai aiþh E ½(Xiþh mX )(X j mX ) n i¼1 j¼1 nh X n 1X ai aiþh CX ( j i h) n i¼1 j¼1
(19:4:55)
A summation matrix similar to that in Fig. 19.4.8 is shown in Fig. 19.4.9. From Fig. 19.4.9 we can write the summation in much the same way as we did in Eq. (19.4.54):
S2 ¼
9 8 h nh nh1 nh1 nh XX X X X X nhj > > > = ai aiþh CX ( j) þ ai aiþh CX ( j) þ ai aiþh CX ( j þ h)> 1< n> > :
j¼0 i¼1
j¼1 Region II
j¼1 i¼jþ1
i¼1 Region III
Region I
> > ; (19:4:56)
19.4
Estimation of Parameters of Random Processes
463
FIGURE 19.4.9
Substituting Eqs. (19.4.55) and (19.4.56) in Eq. (19.4.51), we can write E ½CX (h) as " !# nh h X 2X 2 E ½CX (h) ¼ ai aiþh CX (h) þ sm^ X CX ( j) n j¼0 i¼1 " #" # nhj nh1 X 1 X (ai aiþh þ aiþj aiþhþj ) CX ( j) þ CX (h þ j) n i¼1 j¼1 (19:4:57) where the variance of the sample mean m^ X is as follows: " # n1 X 1 h CX (0) þ 2 s2m^ X ¼ 1 CX (h) n n h¼1
(19:4:48)
The analysis becomes intractable finding the coefficients faig such that E[CX(h)] ¼ CX (h). In the spirit of Eqs. (19.4.5) and (19.4.6), two estimators for the ACF CX (h) can be defined by substituting ai aiþh ¼ ½1=(n h) or ai aiþh ¼ (1=n) in Eq. (19.4.50): CX (h) ¼
nh 1 X (Xi m^ X )(Xiþh m^ X ) n h i¼1
1 C^ X (h) ¼ n
nh X
(Xi m^ X )(Xiþh m^ X )
(19:4:58)
(19:4:59)
i¼1
The expected value E ½C X (h) is obtained by substituting ai aiþh ¼ ½1=(n h) in Eq. (19.4.57) " # h 2X 2 E ½CX (h) ¼ CX (h) þ sm^ X CX ( j) n j¼0 " # nh1 X 2 (n h j)½CX ( j) þ CX (h þ j) (19:4:60) n(n h) j¼1
464
Random Processes
and the expected value E ½C^ X (h) is obtained by substituting ai aiþh ¼ (1=n) in Eq. (19.4.57) "
!# h X h 2 E ½C^ X (h) ¼ 1 CX ( j) CX (h) þ s2m^ X n n j¼0 " # X 2 nh1 2 (n h j)½CX ( j) þ CX (h þ j) n j¼1
(19:4:61)
where s2m^ X in these equations is as given by Eq. (19.4.48): s2m^ X
" # n1 X 1 h CX (0) þ 2 ¼ 1 CX (h) n n h¼1
(19:4:48)
Example 19.4.4 We will follow Example 19.4.3 and find E ½C X (h) and E ½C^ X (h) for the random process whose ACF CX (h) ¼ eajhj with a ¼ 0.5 and the number of data points n ¼ 50. In Example 19.4.3, we have calculated s2m^ X for 50 data points as s2m^ X (50) ¼ 0:0785. E ½C X (h) for n ¼ 50 is obtained from Eq. (19.4.60) as " # h X 2 eaj E ½C X (h) ¼ eah þ s2m^ X (50) 50 j¼0 " # 4X 9h 2 aj a(hþj) (50 h j)½e þ e a ¼ 0:5 50(50 h) j¼1 Similarly, E ½C^ X (h) is obtained from Eq. (19.4.61): # " h h 2 X ah 2 aj ^ þ sm^ X (50) e E ½CX (h) ¼ 1 e 50 50 j¼0 " # 9h 2 4X aj a(hþj) 2 (50 h j)½e þ e a ¼ 0:5 50 j¼1 E ½C X (h), E ½C^ X (h) and the true value CX (h) are shown in Fig. 19.4.10 for lags h ¼ 10 (10% of data points). It is seen that both estimators are biased but there is very little difference between them for the 10 lags shown here.
Variance of Autocovariance Estimators
Finding the variances of C X (h) and C^ X (h) is extremely tedious and is not worth the effort, particularly when the number of data points n is large. Hence the sample mean m^ X can be replaced by the true mean mX in Eqs. (19.4.58) and (19.4.59). The unbiased estimator corresponding to Eq. (19.4.58) is given by C X (h) ¼
nh 1 X (Xi mX )(Xiþh mX ) n h i¼1
(19:4:62)
19.4
Estimation of Parameters of Random Processes
465
FIGURE 19.4.10
and E ½C X (h) ¼ CX (h). The biased estimator corresponding to Eq. (19.4.59) is CX (h) ¼
nh 1X (Xi mX )(Xiþh mX ) n i¼1
(19:4:63)
As a first step toward finding the variance C X (h), we will define a random variable Zi as Zi ¼ (Xi mX )(Xiþh mX ), and Eq. (19.4.62) becomes CX (h) ¼
nh 1 X Zi n h i¼1
From Eq. (19.4.48) the variance of C X (h) can be written as follows: " # nh1 X 1 g Cz (0) þ 2 1 var½CX (h) ¼ CZ (g) nh nh g¼1
(19:4:64)
(19:4:65)
The mean value of Zi is CX (h) for all i and the autocovariance of Zi can be defined by CZ (g) ¼ E ½(Zi CX (h))(Ziþg CX (h)) ¼ E ½Zi Ziþg CX2 (h)
(19:4:66)
E ½Zi Ziþg ¼ E ½(Xi mX )(Xiþh mX )(Xiþg mX )(Xiþgþh mX )
(19:4:67)
where
Since E(Xk mX ) ¼ 0 for all k, we can write E ½Zi Ziþg in terms of the fourth cumulant k4 from Eq. (11.6.8): E ½Zi Ziþg ¼ k4 (h,g) þ CX2 (h) þ CX2 (g) þ CX (g þ h)CX (g h)
(19:4:68)
and substituting this equation in Eq. (19.4.66), we obtain CZ (g) ¼ k4 (h,g) þ CX2 (g) þ CX (g þ h)CX (g h)
(19:4:69)
CZ (0) ¼ k4 (h,0) þ CX2 (0) þ CX2 (h)
(19:4:70)
466
Random Processes
Substituting Eqs. (19.4.69) and (19.4.70) in Eq. (19.4.65), we obtain var½CX (h) 9 8 k4 (h,0) þ CX2 (0) þ CX2 (h) > > > > > > = 1 < nh1 ¼ X g n h> > > 1 ½k4 (h,g) þ CX2 (g) þ CX (g þ h)CX (g h) > > > ; : þ2 n h g¼1 (19:4:71) For a Gaussian process the fourth cumulant k4 is zero, and hence Eq. (19.4.71) becomes 9 8 2 CX (0) þ CX2 (h) > > > > > > = < 1 nh1 var½CX (h) ¼ X g n h> > > 1 ½CX2 (g) þ CX (g þ h)CX (g h) > > > ; :þ2 n h g¼1 (19:4:72) Or, more compactly 1 var½CX (h) ¼ (n h)2
(
nh1 X
) (n h
jgj)½CX2 (g)
h,n
þ CX (g þ h)CX (g h) ,
g¼(nh1)
(19:4:73) Equation (19.4.73) is the discrete counterpart of the continuous process of Eq. (19.4.24) and represents the variance of an unbiased estimator C X (h) of a Gaussian process with known mean value mX. In a similar manner the variance of the biased estimator C^ X (h) of a Gaussian process with known mean value mX given by Eq. (19.4.63) can be obtained as follows: 9 8 2 CX (0) þ CX2 (h) > > > > > > = < n h nh1 var½C^ X (h) ¼ 2 X g n > > > 1 ½CX2 (g) þ CX (g þ h)CX (g h) > > > ; : þ2 n h g¼1 (19:4:74) Example 19.4.5 We will continue the same way as in Example as 19.4.4 and find the variances of the unbiased estimator CX (h) and the biased estimator C^ X (h). We are given CX (h) ¼ e 2ajhj with a ¼ 0.5 and n ¼ 50. From Eq. (19.4.73), var½CX (h) is given by 9 8 1 þ e2ah > > = < 1 nh1 X g var½CX (h) ¼ 2ag a(jgþhj) a(jghj) 1 þe e > ½e n h> ; : þ2 nh g¼1
As in Example 19.4.2, the absolute value signs in the product term eajgþhj eajghj have to be carefully removed. Following the procedure in that example, we can write
19.4
0h
n1 : 2 eajgþhj eajghj ¼
Estimation of Parameters of Random Processes
ea(gþh) ea(gh) ¼ e2ah , ea(gþh) ea(gh) ¼ e2ag ,
467
0gh h,gnh1
n1 , h n: 2 eajgþhj eajghj ¼ ea(gþh) ea(gh) ¼ e2ah ,
0gnh1
The variance equation CX (h), after substituting the equations above, is var½C X (h) 9 8 h X g 2ag > > > > 2ah 2ah > > þ2 1 þe 1þe ½e > > > > n h > > > > g¼1 > > > > > > nh1 = < X g n 1 1 2ag 2ag 1 þe , 0 h þ2 ½e ¼ nh 2 > n h> > > g¼hþ1 > > > > > > > > nh1 X > > g n 1 > > 2ag 2ah > > > > , h n 2 1 þ e , ½e ; : nh 2 g¼1
The variance of the biased estimator C^ X (h) is calculated by substituting (n h)=n2 for 1=(n h) in the preceding equation. Both variances are shown in Fig. 19.4.11. The result is very similar to the one for the continuous case given in Fig. 19.4.6, and the biased estimator gives a better minimum mean-square error. Estimation of Autocorrelation Functions
The two estimators for the autocorrelation function are obtained by substituting mˆ ¼ 0 in the two estimators for the autocovariance function given by Eqs. (19.4.58) and (19.4.59): R X (h) ¼
nh 1 X Xi Xiþh n h i¼1
FIGURE 19.4.11
(19:4:75a)
468
Random Processes
RX (h) is an unbiased estimator since
E ½RX (h) ¼
nh nh 1 X 1 X E ½Xi Xiþh ¼ RX (h) ¼ RX (h) n h i¼1 n h i¼1
1 R^ X (h) ¼ n
nh X
Xi Xiþh
(19:4:75b)
(19:4:76a)
i¼1
R^ X (h) is biased estimator since nh nh 1 X 1 ^ E ½Xi Xiþh ¼ 1 RX (h) E ½RX (h) ¼ n n h i¼1 n
(19:4:76b)
with the bias B ¼ {½RX (h)=n}. Further analyses of these estimators are the same as those of the ACF estimators C X (h) and C^ X (h) with the estimated mean m^ X set equal to 0.
Estimation of Normalized Autocovariance Functions (NACFs)
Estimation of Mean As mentioned earlier, the normalized autocovariance rX (h) (NACF) plays a key role in modeling time series. The NACF for a stationary random process for discrete time can be defined by an equation analogous to Eq. (19.2.16) as follows: rX (h) ¼
CX (h) CX (0)
and CX (h) ¼ rX (h)CX (0)
(19:4:77)
The unbiased estimator C X (h) for large n is defined in Eqs. (19.4.62). The estimated NACF r X (h) for large sample n can be defined as r X (h) ¼
C X (h) C X (0)
(19:4:78)
resulting in a ratio of random variables, and the analysis becomes rather involved. We will now determine the mean of r X (h). Since CX (h) is an unbiased estimator, we can define its 0 0 variations about the mean value CX (h) as C X (h) with E ½C X (h) ¼ 0 and write CX (h) ¼ 0 CX (h)þ C X (h): " # 0 CX (h) CX (h) þ CX (h) E ½rX (h) ¼ E ¼E 0 CX (0) CX (0) þ CX (0) 8 " #1 9 0 > > > C (0) > 0 > > > >½CX (h) þ C X (h) 1 þ X > > = < CX (0) ¼E > > CX (0) > > > > > > > > ; :
(19:4:79)
19.4
Estimation of Parameters of Random Processes
469
0
Assuming that the variations C X (h) are small, we can expand Eq. (19.4.79) in Taylor series, and neglecting second-order terms, we have " # 0 1 CX (h)C X (0) CX (h) 0 E ½rX (h) E CX (h) þ CX (h) ¼E ¼ rX (h) CX (0) CX (0) CX (0)
(19:4:80) 0
Thus, we have an unbiased estimator under the assumption of small variations in C X (h). Estimation of Covariance of NACF Finding the covariance between adjacent values of the estimated NACF r X (h) is very involved and is detailed elsewhere in the literature [8,24,31]. The final result for a Gaussian process X(t) is as follows:
cov½rX (h), r X (h þ g) 9 8 1 > > ½rX (k)rX (k þ g) þ rX (k þ h þ g)rX (k h) > > > > > > nh > > > > > > 2 > > > > r (h)r (k)r (k h g) > > X X X = < 1 X nhg ¼ > > 2 r (h þ g)r (k)r (k h) > k¼1> > > X X > > nh X > > > > > > > > > > 2 > > ; : þ r2 (k)r (h)r (h þ g) X X X n
(19:4:81)
The variance of r X (h) is obtained by substituting g ¼ 0 in Eq. (19.4.81). The result is 9 8 1 > 1 > = < ½r2X (k) þ rX (k þ h)rX (k h) X nh (19:4:82) var½rX (h) ¼ 4 2 > > k¼1: rX (h)rX (k)rX (k h) þ r2X (k)r2X (h) ; nh n Large-Lag Approximations
For most stationary random processes the NACF becomes negligibly small after some lag q, that is, rX (h) 0, jhj . q. Hence, under the assumption rX(h) ¼ 0 for jhj . q in Eq. (19.4.81), we can write the large-lag covariance as cov½rX (h), r X (h þ g)
q 1 X r (k)rX (k þ g), n h k¼q X
jhj . q
(19:4:83)
Similarly, the large-lag variance after substituting rX(h)¼0 for jhj . q in Eq. (19.4.82) is var½rX (h)
q 1 X 2 r (k), n h k¼q X
jhj . q
(19:4:84)
If the estimator for ACF is C^ X (h), then the divisor (n 2 h) in Eqs. (19.4.83) and (19.4.84) is replaced by the divisor n. Thus the variance equation [Eq. (19.4.84)] becomes var½^rX (h)
q 1X 2 r (k), n k¼q X
jhj . q
(19:4:85)
470
Random Processes
It has been shown [3,25] that if the true NACF rX(h) is zero for jhj . q, then the estimated NACF rX(h) is normally distributed with zero mean and variance given by Eq. (19.4.84). This property is called Anderson’s theorem.
Discrete White-Noise Process
A zero mean stationary discrete white-noise process fXig has the following property: E ½Xi Xj ¼ dji where the Kro¨necker delta dji ¼ 1, j ¼ i and dji ¼ 0, j = i, an equation analogous to Eq. (19.2.17) for continuous white noise. As a consequence, the NACF rX(h) has the property 1, h ¼ 0 (19:4:86) rX (h) ¼ 0, h = 0 Hence the variance of the estimated r X (h) for white noise is obtained by substituting rX (k) ¼ 0 for k = 0 in Eq. (19.4.84) to yield var½rX (h)
1 nh
(19:4:87)
According to Anderson’s theorem, the estimated NACF r X (h) of a stationary discrete white-noise process is Gaussian-distributed with zero mean and variance 1=(n h). As a result, we can construct a test for white noise from knowledge of r X (h). Since the pffiffiffiffiffiffiffiffiffiffiffi variance is 1=(n h), the 95% confidence interval (+2s) is +2= n h. If the estimated NACF is given by r^ X (h) ¼
C^ X (h) C^ X (0)
(19:4:88)
then, from Eq. (19.4.85), the variance of the estimator r^ X (h) is 1 n 2ffiffi p and the corresponding confidence interval is + n. var½^rX (h)
(19:4:89)
Example 19.4.6 We have generated 100 points of a zero mean unit variance Gaussian white noise fXig by the computer. The plot is shown in Fig. 19.4.12. The estimated NACFs r X (h) and r^ X (h) of this sequence were determined from Eqs. (19.4.78) and (19.4.88) and plotted in Fig. 19.4.13 along with their confidence intervals for 50 lags. In the plot of the estimator r X (h), only two points out of 50 (4%) exceed the confidence limits, whereas in the estimator r^ X (h), no points exceed the confidence limits. Both estimators pass the white-noise test at the 5% significance level comfortably, and hence we conclude that the process fXig is indeed a white-noise sequence. Example 19.4.7 We will generate 100 points of the discrete-time process for f ¼ 0.8 and 20.8 in Example 19.2.11 with s2X ¼ 1 Xi ¼ fXi1 þ ni , i ¼ 1, . . . , 100 and estimate the NACF and compare it to the true NACF. The computer-generated random sequence is shown in Fig. 19.4.14 for f ¼ 0.8 and 20.8. The figure shows that for f ¼ 20.8, the process is more oscillatory. The
19.4
Estimation of Parameters of Random Processes
FIGURE 19.4.12
FIGURE 19.4.13
FIGURE 19.4.14
471
472
Random Processes
FIGURE 19.4.15
estimated NACFs r X (h) and r^ X (h) are determined from Eqs. (19.4.78) and (19.4.88) respectively. They are shown in Fig. 19.4.15 for f ¼ 0.8 and 20.8 for 10 lags. Here also the NACFs for f ¼ 20.8 show oscillatory behavior. For the 10 lags shown,there is very little difference between the two estimators r X (h) and r^ X (h). However, the discrepancy between the true NACF and the estimators for higher lags is to be expected.
B 19.5
POWER SPECTRAL DENSITY
19.5.1 Continuous Time In signal analysis power spectra are associated with Fourier transforms that transform signals from the time domain to the frequency domain. The same concept is also applicable to stationary random processes. The correlation functions represent stationary processes in the time domain. We can transform them to the frequency domain by taking their Fourier transforms. The power spectral density (psd) function SX (v) of a real stationary random process X(t) is defined as the Fourier transform of the autocorrelation function: ð1 RX (t)ejvt dt (19:5:1) SX (v) ¼ FT½RX (t) ¼ 1
From the Fourier inversion theorem we can obtain the autocorrelation function from the power spectral density: ð 1 1 RX (t) ¼ IFT½SX (v) ¼ SX (v)e jvt dv (19:5:2) 2p 1 Equations (19.5.1) and (19.5.2) are called the Wiener –Khinchine theorem. Since RX(0) ¼ E [X 2(t)], the average power in the random process, we obtain the following from Eq. (19.5.2): RX (0) ¼ E ½X 2 (t) ¼
1 2p
ð1
ð1 SX (v)dv ¼
1
SX ( f )df 1
(19:5:3)
19.5
Power Spectral Density
473
Thus, SX ( f ) represents the average power per hertz, and hence the term power spectral density. Since RX (t) is an even function, we can rewrite Eq. (19.5.1) as ð1 SX (v) ¼
ð1 RX (t)½cos(vt) þ j sin(vt)dt ¼
RX (t) cos(vt)dt
1
(19:5:4)
1
and SX (v) is also an even function. Hence Eq. (19.5.2) can be rewritten as RX (t) ¼
1 2p
ð1 SX (v) cos(vt)dv
(19:5:5)
1
The cross-spectral density (csd) SXY(v) of two real stationary random processes X(t) and Y(t) is defined as the Fourier transform of the cross-correlation function, RXY(t) ð1
RXY (t)ejvt dt
SXY (v) ¼ FT½RXY (t) ¼
(19:5:6)
1
and the inverse Fourier transform of SXY(v) gives the cross-correlation function: RXY (t) ¼ IFT½SXY (v) ¼
1 2p
ð1
SXY (v)e jvt dv
(19:5:7)
1
The cross-spectral density SXY(v) will be complex, in general, even when the random processes X(t) and Y(t) are real. The Fourier transforms can also be obtained from the tables in Appendix A. Example 19.5.1 The power spectral density (psd) of the random binary wave of Example 19.2.6 is to be determined. The autocorrelation of the random process X(t) is given by 8 <
jtj A 1 , RX (t) ¼ T : 0, 2
jtj , T otherwise
The psd SX (v) given by ð0 SX (v) ¼
ðT t t A2 1 þ ejvt dt þ A2 1 ejvt dt T T T 0
A2 ½(1 e jvT þ jvT ) þ (1 ejvT jvT ) v2 T sin(vT=2) 2 ¼ A2 T vT=2 ¼
is shown in Fig. 19.5.1. As we can see from figure, the psd is an even function. Example 19.5.2 We will find the psd from the autocorrelation RX (t) ¼ 14½1 þ e2ljtj of the random telegraph wave given in Example 19.2.7. Taking the Fourier transform, the psd
474
Random Processes
FIGURE 19.5.1
is given by SX (v) ¼
1 4
ð1 1
ejvt dt þ
1 4
ð0
e2lt ejvt dt þ
1
1 4
ð1
e2lt ejvt dt
0
1 4l p l ¼ 2pd(v) þ 2 ¼ d(v) þ 2 2 4 2 4l þ v 4l þ v2 The impulse function (p=2)d(v) represents the direct-current (dc) value of RX (t) ¼ 14. The psd is shown in Fig. 19.5.2 for l ¼ 1. Example 19.5.3 We will find the psd from the autocorrelation RX (t) ¼ e2ljtj cos(v0 t) of Example 19.2.8. We can take the Fourier transform of RX(t) directly, but it is easier to
FIGURE 19.5.2
19.5
Power Spectral Density
475
evaluate the FT by using the frequency convolution property of the FT as follows: FT½e2ljtj ¼
4l2 4l2 þ v2
and
FT½cos(v0 t) ¼ p½d(v þ v0 ) þ d(v v0 )
Using the frequency convolution property x(t)y(t) , (1=2p)X(v) Y(v), we have e2ljtj cos(v0 t) () or
1 4l2 p½d(v þ v0 ) þ d(v v0 ) 2p 4l2 þ v2
1 1 þ SX (v) ¼ 2l 4l2 þ (v þ v0 )2 4l2 þ (v v0 )2
and simplifying, we obtain SX (v) ¼
4l(v2 þ v20 þ 4l2 ) v4 2(v20 4l2 )v2 þ (v20 þ 4l2 )2
With l ¼ 1 and v0 ¼ 2p, the psd becomes SX (v) ¼
4(v2 þ 4p2 þ 4l2 ) v4 8(p2 1)v2 þ 16(p2 þ 1)2
The psd SX(v) is shown in Fig. 19.5.3. Example 19.5.4 (Bandlimited Process) The autocorrelation function RX (t) ¼ sin(v0 t)=pt of a stationary random process X(t) is shown in Fig. 19.5.4 for v0 ¼ 2p. We have to find the psd SX(v). From item 2 in the FT table, the Fourier transform of RX (t) can be obtained as follows: sin(v0 t) () pv0 (v), hence SX (v) ¼ pv0 (v) pt The psd SX(v) is shown in Fig. 19.5.5 for v0 ¼ 2p. The random process that has a psd as in Fig. 19.5.5 is called a bandlimited signal since the frequency spectrum exists only between –2p and 2p.
FIGURE 19.5.3
476
Random Processes
FIGURE 19.5.4
FIGURE 19.5.5
Example 19.5.5 (Bandlimited Process) RX (t) ¼
The autocorrelation function RX(t)
sin½v0 (t t0 ) sin½v0 (t þ t0 ) þ p(t t0 ) p(t þ t0 )
of a bandlimited random process X(t) is shown in Fig. 19.5.6 for v0 ¼ 2p and t0 ¼ 3.
FIGURE 19.5.6
19.5
Power Spectral Density
477
Since sin(v0 t)=pt , pv0 (v) using the time-shifting property x(t + t0 ) , X(v)e+jvt0 of the FT, we have sin½v0 (t t0 ) () pv0 (v)ejvt0 , p(t t0 )
sin½v0 (t þ t0 ) () pv0 (v)e jvt0 p(t þ t0 )
or sin½v0 (t t0 ) sin½v0 (t þ t0 ) þ () pv0 (v)½e jvt0 þ ejvt0 p(t t0 ) p(t þ t0 ) and SX (v) ¼ 2pv0 (v) cos(vt0 ) The psd is shown in Fig. 19.5.7 for v0 ¼ 2p and t0 ¼ 3.
Example 19.5.6 (Bandpass Process) RX (t) ¼
The autocorrelation function RX(t) 2 sin(v0 t) cos(vc t) pt
of a bandpass random process X(t) is shown in Fig. 19.5.8 for v0 ¼ 2p and vc ¼ 16p. We will find the psd using the frequency convolution property of the FT: xðtÞyðtÞ ()
1 XðvÞ YðvÞ 2p
Since 2 sin(v0 t)=pt , 2pv0 (v) and cos(vc t) , p½d(v þ vc ) þ d(v vc Þ, we have 2 sin(v0 t) 1 cos(vc t) () 2pv0 (v) p½d(v þ vc ) þ d(v vc ) pt 2p
FIGURE 19.5.7
478
Random Processes
FIGURE 19.5.8
and the psd SX (v) ¼ pv0 (v þ vc ) þ pv0 (v vc ) The function SX(v) is shown in Fig. 19.5.9 for v0 ¼ 2p and vc ¼ 16p. Example 19.5.7 (White Noise) A white-noise process has the autocorrelation function RX (t) ¼ s2X d(t) given in Eq. (19.2.17). The psd of white noise is given by SX (v) ¼ s2X ,
1 , v , 1
and has a flat spectrum. Since the process has all frequencies with equal power, it is called “white Ðnoise,” analogously to white light. It has infinite energy since 1 RX (0) ¼ (1=2p) 1 s2X dv ¼ 1, and hence it is an idealization. Example 19.5.8 The cross-correlation function of two random processes X(t) ¼ A cos(v0 t) þ B sin(v0 t) and Y(t) ¼ A sin(v0 t) þ B cos(v0 t) in Example 19.2.9 is RXY (t) ¼ s2 sin(v0 t), E[A 2] ¼ E[B 2] ¼ s2. We will find the cross-spectral density SXY(v). Taking FT of s2 sin(v0 t), from tables, we have SXY (v) ¼ jps2 ½d(v þ v0 ) d(v v0 ) and this is shown in Fig. 19.5.10. Note that SXY(v) is neither even nor real.
FIGURE 19.5.9
19.5
Power Spectral Density
479
FIGURE 19.5.10
Example 19.5.9 The cross-spectral density is to be found for the cross-correlation function RXY(t) derived in Example 19.2.10: 8 > 4 (t=2) 1e2t , t . 0 1 < 15 e 6 RXY (t) ¼ þ 1 2 > 2t : e , t0 10 Taking FT of each term in RXY(t), we have 4 (t=2) 4 1 e ; () 15 15 1=2 þ jv 1 () pd(v); 2
1 2t 1 1 e () 6 6 2 þ jv
1 2t 1 1 e () 10 10 2 jv
The cross-spectral density SXY(v) is obtained by adding the FT terms shown above: SXY (v) ¼ pd(v) þ
4 1 1 1 1 1 1 8 þ 2v2 þ 3jv þ ¼ þ pdðvÞ þ 15 12 þ jv 6 2 þ jv 10 2 jv 3 (1 þ 2jv)(4 þ v2 )
The cross-spectral density SXY(v) is complex and possesses no symmetry, unlike the power spectral density. The constant term 12 in RXY(t) gives rise to the impulse function in the frequency domain. The amplitude jSXY (v)j and the phase argfSXY(v)g spectra are graphed in Fig. 19.5.11. Properties of Power Spectral Densities of Stationary Random Processes 1. SX(v) is a real function. In general, the Fourier transform X(v) of any function X(t) will be complex. However, the ACF RX(t) is an even function and satisfies the relation RX(t) ¼ RX(2t). Hence, from the definition of psd, we obtain ð1 ð1 RX (t)ejvt dt ¼ RX (t)½cos(vt) þ j sin(vt)dt SX (v) ¼ 1
1
ð1
RX (t) cos(vt)dt
¼ 1
Ð1 since the imaginary part 1 RX (t) j sin(vt)dt ¼ 0 because an even function multiplying an odd function results in an odd function and the integral of an odd function over (21,1) is zero. 2. From property 1 the psd SX(v) is an even function and, hence it is a function of v2. As a consequence SX(v) ¼ SX(2v). (19.5.8) 3. SX(v) 0: The psd is a nonnegative function of v. (19.5.9)
480
Random Processes
FIGURE 19.5.11
4. From the Fourier transform properties: ð ð1 1 1 SX (v)dv ¼ SX ( f )df ¼ E ½X 2 (t) RX (0) ¼ 2p 1 1 ð1 RX (t)dt SX (0) ¼
(19:5:10)
1
5. If Z(t) ¼ X(t) þ Y(t), then from Eq. (19.2.30), we have RZ (t) ¼ RX (t) þ RY (t) þ RXY (t) þ RYX (t) SZ (v) ¼ SX (v) þ SY (v) þ SXY (v) þ SYX (v) If X(t) and Y(t) SZ (v) ¼ SX (v) þ SY (v)
are
orthogonal,
then
Eq.
(19.5.11)
(19:5:11) reduces
to
Alternate Form for Power Spectral Density
The psd can also be obtained directly from the stationary random process X(t). We will truncate X(t) in the interval (2T, T ) and define the random process XT (t) as X(t), T t T XT (t) ¼ (19:5:12) 0, otherwise ÐT The FT XT (v) of XT (t), given by XT (v) ¼ T X(t)ejvt dt, is a random variable. The quantity ST (v), defined by
2
ð
jXT (v)j2 XT (v)XT (v) 1
T jvt
(19:5:13) ¼ X(t)e dt ST (v) ¼
¼ 2T
2T T 2T is called the periodogram. The periodogram represents the power of the sample function X(t) at the frequency v. Equation (19.5.13) can be expanded as follows: ð ð 1 T T ST (v) ¼ X(t)X(s)ejv(ts) dt ds (19:5:14) 2T T T We can make the transformation t ¼ t 2 s and u ¼ s in Eq. (19.5.14) and perform the integration in the (t, u) plane. The Jacobian k J k of the transformation from Eq. (19.3.5) is 1,
19.5
Power Spectral Density
481
and Eq. (19.5.14) can be transformed as follows: ST (v) ¼
1 2T
ð ð
X(u)X(u þ jtj)du ejvt dt
(19:5:15)
We can obtain the limits of integration from Fig. 19.3.1 and write Eq. (19.5.15) as ð 2T ð T 1 ST (v) ¼ X(u)X(u þ t)du ejvt dt 2T Tþt 0 ð 0 ð Tjtj 1 X(u)X(u þ t)du ejvt dt þ 2T 2T T ð0 ð 2T R^ X (t)ejvt dt þ R^ X (t)ejvt dt ¼ 0
2T
ð 2T
R^ X (t)ejvt dt
¼
(19:5:16)
2T
where we have used the definition for R^ X (t) as in Eq. (19.4.34a). Using Eq. (19.4.34b), the expected value of ST(v) can be written as ð 2T h ð 2T i jtj jvt jvt ^ E½ST (v) ¼ E RX (t) e dt ¼ RX (t) 1 dt (19:5:17) e 2T 2T 2T Taking the limit of E½ST (v) in Eq. (19.5.17) as T ! 1, we have ð 1
jtj jvt RX (t) 1 dt lim E½ST (v) ¼ lim e T!1 T!1 2T 1 jtj ¼ lim FT RX (t) 1 T!1 2T
(19:5:18)
Using the frequency convolution property of the FT, we obtain 1 1 X(v) Y(v) ¼ x(t)y(t) () 2p 2p
ð1 X(p)Y(v p)dp 1
and with RX (t) , SX (v) and jtj sin2 vT 1 , 2T 2T (vT )2 Eq. (19.5.18) can be rewritten as follows: jtj lim E½ST (v) ¼ lim FT RX (t) 1 T!1 T!1 2T ð1 2 sin (v p)T SX (p)T dp ¼ lim T!1 1 p½(v p)T 2
(19:5:19)
482
Random Processes
From Ref. 36, lim T
T!1
sin2 (vT ) ! d(v) and p(vT )2
lim T
T!1
sin2 (v p)T ¼ d(v p) p½(v p)T 2
and substituting this result in Eq. (19.5.19), we have ð1 lim E½ST (v) ¼
T!1
SX (p)d(v p)dp ¼ SX (v)
(19:5:20)
1
Thus, the expected value of the periodogram as T ! 1 yields the psd SX(v).
Estimation of Power Spectral Density
We have defined an estimator R^ X (t) for the autocorrelation function in Eq. (19.4.34a). It is intuitive to express the estimator S^ X (v) for the psd as the FT½R^ X (t) as follows: S^ XT ðvÞ ¼
ðT
R^ X ðtÞejvt dt : S^ X ðvÞ ¼ lim S^ XT ðvÞ T!1
T
(19:5:21)
This equation corresponds to Eq. (19.5.16). The periodogram is an asymptotically unbiased estimator for SX(v) since limT!1 E½S^ X (v) ¼ SX (v). We may be tempted to conclude that since R^ X (t) is a consistent estimator of RX(t), the Fourier transform of R^ X (t) will also be a consistent estimator of SX(v). This is not true because S^ X (v) fails to converge to SX(v) for T ! 1. We will show heuristically that var½S^ XT (v) does not approach 0 as T ! 1. From Eq. (19.5.14) the second moment E ½S^ XT (v)2 is given by ð ð ð ð h i2 1 T T T T E S^ XT (vÞ ¼ 2 E ½X(t)X(s)X(v)X(u) 4T T T T T ejv(tsþvu) dt ds dv du
(19:5:22)
If the process X(t) is Gaussian, then from Eq. (11.6.8) or Example 19.3.4, we obtain E ½X(t)X(s)X(v)X(u) ¼ RX (t s)RX (v u) þ RX (t v)RX (s u) þ RX (t u)RX (s v)
(19:5:23)
Substituting Eq. (19.5.23) into Eq. (19.5.22), we have 1 E ½S^ XT (v)2 ¼ 2 4T
ðT ðT ðT ðT ½RX (t s)RX (v u) T
T
T
T
þ RX (t u)RX (s u) þ RX (t u)RX (s v) ejv(tsþvu) dt ds dv du
(19:5:24)
19.5
Power Spectral Density
483
Rearranging Eq. (19.5.24), we obtain 1 E ½S^ XT (v)2 ¼ 2T
ðT ðT T
1 2T þ
1 2T
1 2T þ
1 2T
1 2T
RX (t s)ejv(ts) dt ds
T
ðT ðT T
RX (v u)ejv(vu) dv du
T
ðT ðT T
T
T
T
T
T
T
T
ðT ðT ðT ðT
ðT ðT
RX (t u)ejv(tu) dt du RX (v s)ejv(vs) dv ds RX (t v)ejv(tþv) dt dv RX (s u)e jv(sþu) ds du
(19:5:25)
Using Eq. (19.5.21), we can simplify Eq. (19.5.25) as follows: (
2 )
ð T ð T
1 jv(tþv)
E ½S^ XT (v) ¼ 2{E ½S^ XT (v)}2 þ R (t v)e dt dv X
4T 2 T T 2
(19:5:26)
As T ! 1, the second term on the righthand side of Eq. (19.5.26) tends to zero. Hence E ½S^ XT (v)2 2{E ½S^ XT (v)}2 ,
v=0
Subtracting {E ½S^ XT (v)}2 from both sides of this equation, we obtain E ½S^ XT (v)2 {E ½S^ XT (v)}2 {E ½S^ XT (v)}2 ,
v=0
or var½S^ XT (v) {E ½S^ XT (v)}2 , v = 0
(19:5:27)
Substitution of Eq. (19.5.20) limT!1 E ½SXT (v) ¼ SX (v), in Eq. (19.5.27) results in lim var½S^ XT (v) S2X (v),
T!1
v=0
(19:5:28)
Thus, as T ! 1, var½S^ XT (v) does not go to zero but is approximately equal to the psd S2X (v), and hence S^ X (v) is not a consistent estimator. To make S^ X (v) consistent, spectral windowing [25] is employed. However, with spectral windowing the asymptotically unbiased nature of the estimator is lost.
484
Random Processes
19.5.2 Discrete Time As in Eq. (19.5.1), we can also define power spectral density for a discrete-time stationary ergodic random process fXi ¼ X(ti), i ¼ 0, +1, . . .g, where the intervals are equally spaced. The autocorrelation function RX(h) has been defined in Eq. (19.2.34). The psd SX(v) is the Fourier transform of RX(h) and is given by 1 X SX (v) ¼ FT½RX (h) ¼ RX (h)ejvh (19:5:29) h¼1
Since the discrete-time Fourier transforms are periodic, the psd for discrete-time random process Xi as given by Eq. (19.5.29) is periodic with period 2p. The inverse FT of SX(v) is the autocorrelation function RX(h), given by ð 1 p SX (v)e jvh dv (19:5:30) RX (h) ¼ IFT½SX (v) ¼ 2p p Example 19.5.10 (Discrete Analog of Example 19.5.1) The autocorrelation RX(h) of the discrete-time process fXig corresponding to the continuous-time process X(t) of Example 19.5.1 is given by 8 jhj < 2 A 1 , jhj , n RX (h) ¼ n : 0, otherwise and is shown in Fig. 19.5.12 for A ¼ 1 and n ¼ 10. The psd is obtained from Eq. (19.5.29) for finite n as n1 X jhj jvh 2 SX (v) ¼ A 1 e n h¼(n1) " # 1 n1 X h jvh X h jvh 2 ¼A 1þ 1þ e þ 1 e n n h¼(n1) h¼1 " # " # n1 n1 X X h h ¼ A2 1 þ 1 1 e jvh þ ejvh ¼ A2 1 þ 2 cos(vh) n n h¼1 h¼1 A2 1 cos(nv) A2 sin(nv=2) 2 ¼ ¼ n 1 cos(v) n sin(v=2) and SX (0) ¼
A 2 n2 ¼ A2 n n
The psd is graphed in Fig. 19.5.13 for A ¼ 1 and n ¼ 5 for values of v between – 2p and 2p, and the periodic nature of the psd is evident. Figure 19.5.13 is similar to Fig. 19.5.1 except for the periodicity. Example 19.5.11 (Discrete Analog of Example 19.5.2) The discrete AC function RX(h) corresponding to Example 19.5.2 is given by RX (h) ¼ s2X 1 þ e2ljhj , 1 , h , 1
19.5
Power Spectral Density
485
FIGURE 19.5.12
where 1 is a discrete unity function. The psd is determined as follows:
SX (v) ¼
1 X
s2X 1 þ e2ljhj ejvh
h¼1
"
¼
1 X
s2X
h¼1
( ¼
s2X (
¼
s2X
1 X h¼1
1:e
jvh
þ
1 X
e
2lh jvh
e
h¼1
þ
1 X
# e
2lh jvh
e
h¼1
1 X 2pd(v 2ph) þ 1 þ eh(2lþjv) þ eh(2ljv) h¼1
1 X
1 e4l 2pd(v 2ph) þ 4l 1þe 2e2l cos(v) k¼1
)
)
where d(v) is the Dirac delta function. The delta function train corresponds the discrete constant s2X ¼ 14 in the frequency domain. The plot of SX(v) is shown in Fig. 19.5.14
FIGURE 19.5.13
486
Random Processes
FIGURE 19.5.14
with s2X ¼ 15 and l ¼ 15. The graph is very similar to Fig. 19.5.2 except that it is periodic with period equal to 2p. Alternate Form for Power Spectral Density
The discrete psd can also be obtained from the stationary discrete-time random process fXkg. We will truncate this process in the interval (0, N – 1) and write 0k N1 Xk , (19:5:31) XkN ¼ 0, otherwise PN1 The discrete-time FT of the sequence fXkNg is given by XN (v) ¼ k¼0 Xk ejvk , which is a random variable. We now define the quantity SN(v) as
2
1
jX (v)j2 X (v)X (v) 1
NX N N jvk
N (19:5:32) ¼ Xk e SN (v) ¼
¼
N k¼0 N N which is the periodogram for the discrete random process. Equation (19.5.32) can be expanded as follows: SN (v) ¼
N 1 N 1 X 1X Xk Xm ejv(km) N m¼0 k¼0
(19:5:33)
Substituting h ¼ (k 2 m) and j ¼ m in Eq. (19.5.33), the summation is carried out along the diagonal with limits found by referring to Fig. 19.4.1, resulting in " # (N1)=2 N1 X X 1 Xm Xmþh ejvh SN (v) ¼ N m¼½(N1)=2þh h¼0 (19:5:34) " # ½(N1)=2jhj 0 X X 1 jvh þ Xm Xmþh e N m¼½(N1)=2 h¼(N1) The estimated autocorrelation R^ X (h) from Eq. (19.4.76) is X 1 Nh1 R^ X (h) ¼ Xi Xiþh N i¼0
(19:5:35)
19.5
Power Spectral Density
487
and substituting Eq. (19.5.35) in Eq. (19.5.34), we obtain SN (v) ¼
N1 X h¼0
R^ X (h)ejvh
h¼(N1)
N 1 X
¼
0 X
R^ X (h)ejvh þ R^ X (h)ejvh
(19:5:36)
h¼(N1)
Taking expectations and substituting Eq. (19.4.76b) in Eq. (19.5.36), we obtain E ½SN (v) ¼
N1 X
E ½R^ X (h)ejvh ¼
h¼(N1)
N1 X jhj 1 RX (h)ejvh N h¼(N1)
(19:5:37)
Taking the limit as of Eq. (19.5.37) as N ! 1, we have N 1 X
jhj lim E ½SN (v) ¼ lim 1 RX (h)ejvh N!1 N!1 N h¼(N1) jhj ¼ lim DTFT RX (h) 1 N!1 N
(19:5:38)
We use the frequency convolution property of the DTFT ð 1 1 p X(v) Y(v) ¼ X(p)Y(v p)dp xn yn () 2p 2p p with RX (h) , SX (v) and from Example 19.5.10 jhj 1 sin(vN=2) 2 1 () N N sin(v=2) Eq. (19.5.38) can be written as jhj lim E ½SN (v) ¼ lim DTFT RX (h) 1 N!1 N!1 N ðp 1 1 sin½(v p) N=2 ¼ lim SX (p) dp N!1 2p p N sin½(v p)=2
(19:5:39)
Using the result 1 X 1 sin(vN=2) d(v 2kp) lim ! N!1 N sin(v=2) k¼1 in Eq. (19.5.39), we obtain 1 N!1 2p
ðp
lim E ½SN (v) ¼ lim
N!1
SX (p)d(v p)dp ¼ SX (v) p
(19:5:40)
488
Random Processes
Thus, the periodogram SN(v) is an asymptotically unbiased estimator of the psd SX(v). The variance of SN(v) can be obtained by using techniques similar to the continuous case, and we can show a result similar to Eq. (19.5.28): lim var½SN (v) S2X (v), v = 0
N!1
(19:5:41)
This equation shows that the estimator SN(v) is not a consistent estimator of SX(v). Example 19.5.12 To check the validity of Eq. (19.5.41), a computer simulation of discrete white noise of zero mean and unit variance was performed. The power spectral density of the white noise is the variance, or SX(v) ¼ s2X ¼ 1. The number 2m of data points with m ¼ 7,9,10,11 were chosen so that they could fit into the discrete Fourier transform algorithm. The psd S(v) was estimated for N ¼ 128, 512, 1024, and 2048 points using Eq. (19.5.32). Because of the symmetry about N/2, the estimates S128(v),
FIGURE 19.5.15
TABLE 19.5.1. #
PSD
N
Mean
Variance
1 2 3 4 5
SX(v) S128(v) S512(v) S1024(v) S2048(v)
— 128 512 1024 2048
1 1.0045 0.9944 0.9971 0.9987
0 0.9214 0.8908 0.9941 1.0240
19.5
Power Spectral Density
489
S512(v), S1024(v), and S2048(v) are graphed for only half the number of data points in Figs. 19.5.15a –19.5.15d. The estimated psd’s and their variances are shown in Table 19.5.1. The table shows that the estimator SN(v) is unbiased because the means for all N are nearly equal to 1 as expected. However, the variances of the psd for all N are nearly the same, equaling the derived value s4X ¼ 1, which is the expected result according to Eq. (19.5.41). Thus, the simulation of discrete white noise confirms the desired theoretical result.
CHAPTER
20
Classification of Random Processes
B 20.1
SPECIFICATIONS OF RANDOM PROCESSES
In the last chapter we discussed the fact that a random process X(t) is a random variable indexed on a time parameter and the random processes were classified as only nonstationary and stationary. We can also give other specifications of random processes depending on time t and the state X. If the timepoints are a set of points like {tn , n ¼ 0, +1, +2, . . .}, they are discrete, and if they are intervals of time on the real line such as {t T} or {T1 , t , T2 }, they are continuous. The state of a process is the set of values of a sample function Xi or X(t). If this set is finite or countably infinite, the state space is discrete and if it is uncountably infinite, the state space is continuous. On the basis of these definitions, we can specify four sets of random processes as follows.
20.1.1 Discrete-State Discrete-Time (DSDT) Process Example 20.1.1 The random variable Xn denotes the position of a particle at time n. At each time i the particle undergoes a jump of þ1 with probability p and a jump of 21 with probability q and stays in the same position with probability 1 2 p 2 q. The jumps are independent. If Yi is a random variable representing the jump at the ith step, then the sequence {Yi , i ¼ 1, 2, . . .} are independent with P{Yi ¼ 1} ¼ p, P{Yi ¼ 1} ¼ q and P{Yi ¼ 0} ¼ 1 p q. Initially the particle is at X0 ¼ 0. The DSDT process Xi is given by Xi ¼ Xi1 þ Yi ,
i ¼ 1, . . . , n
This process, called random walk, is represented in Fig. 20.1.1.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
490
(20:1:1)
20.1 Specifications of Random Processes
491
FIGURE 20.1.1
20.1.2
Discrete-State Continuous-Time (DSCT) Process
Example 20.1.2 A random process N(t) has an increment of unity at each random time tk where the random times {tk } are Poisson-distributed with l as the occurrence of events per unit time. The probability of occurrence of k events in the time interval (t1, t2) is given by P{N(t2 ) N(t1 ) ¼ k} ¼
l(t2 t1 )k l(t2 t1 ) e , k!
k ¼ 0, 1, . . . ,
(20:1:2)
Such a process is known as a Poisson or counting process and is shown in Fig. 20.1.2.
20.1.3
Continuous-State Discrete-Time (CSDT) Process
Example 20.1.3 In Example 20.1.1 if the jumps are continuously distributed with probability density function fX(x) as shown in Fig. 20.1.3, then we have a continuousstate discrete-time process. This process is also a random walk with continuous state space. It also characterizes a sample function discretized at equal intervals of time.
20.1.4
Continuous-State Continuous-Time (CSCT) Process
Example 20.1.4 In this process both the state and the time are continuous. For example, in the extrusion of plastic bags, the thickness of the plastic sheet will be continuously
FIGURE 20.1.2
492
Classification of Random Processes
FIGURE 20.1.3
FIGURE 20.1.4
varying with respect to time with the statistics being constant over long periods of time. In the random-walk example (Example 20.1.1), if both the differential time Dt and the differential state Dx tend to zero, and if certain limiting conditions are satisfied, then we get a Brownian motion whose sample function is shown in Fig. 20.1.4. We will now discuss different types of random processes.
B 20.2
POISSON PROCESS
A random process {N(t), t 0} represents the total number of events (detection of photons or electrons) that have occurred prior to time t. Denoting the time of occurrence of an event as a random variable T, we make the following assumptions about the process {N(t), t 0}: 1. Pfsingle event T in the interval t, t þ Dtg ¼ P{N(t þ Dt) N(t) ¼ 1} ¼ P{N(Dt) ¼ 1} ¼ lDt þ o(Dt), where any function g(x) is of o(Dt) if limDt!0 ½g(Dt)=Dt ! 0: 2. P{no event in the interval t,t þ Dt} ¼ P{N(t þ Dt) N(t) ¼ 0} ¼ P{N(Dt) ¼ 0} ¼ 1 lDt þ o(Dt): 3. P{more than two events in the interval t,t þ Dt} ¼ P{N(Dt) 2} ¼ o(Dt): 4. Events occurring in nonoverlapping time intervals are statistically independent: P{½N(t2 ) N(t1 )½N(t4 ) N(t3 )} ¼ P{½N(t2 t1 )½N(t4 t3 )} ¼ P{N(t2 t1 )}P{N(t4 t3 )}
for
t1 , t2 , t3 , t4
(20:2:1)
20.2
Poisson Process
493
FIGURE 20.2.1
Processes satisfying Eq. (20.2.1) are called independent increment processes. Given these assumptions, we will find the probability of n events occurring in time interval t: P{n events in the time interval t} ¼ P{N(t) ¼ n} ¼ PN (n, t) We will first find the probability of no event in the interval t þ Dt ¼ PN {0, t þ Dt}. The interval (0,t þ Dt) can be partitioned as (0,t) and (t,t þ Dt) as shown in Fig. 20.2.1. Thus, PN {0, t þ Dt} can be given as probability of no event in (0,t) and probability of no event in (t,t þ Dt). Since these two intervals are nonoverlapping, we can write from assumption 4 PN {0, t þ Dt} ¼ PN {0, t}PN {0, Dt}
(20:2:2)
and from assumption 2 PN (0, t þ Dt) ¼ PN (0, t)(1 lDt) þ o(Dt) or PN (0, t þ Dt) PN (0, t) ¼ PN (0, t)lDt þ o(Dt)
(20:2:3)
Dividing Eq. (20.2.3) by Dt and taking the limit as Dt ! 0, we have dPN (0, t) ¼ lPN (0, t) dt
(20:2:4)
The initial condition for this first-order differential equation is obtained from Pfno events in zero timeg equals 1. Hence the solution to Eq. (20.2.4) with initial condition PN(0, 0) ¼ 1 is given by PN (0, t) ¼ elt u(t) which is the probability that no event T occurs in the time interval (0, t). Or P{N(t) ¼ 0} ¼ PN (0, t) ¼ P(T . t) ¼ elt u(t)
(20:2:5)
Hence, the probability of occurrence of an event T in the interval (0, t) is the complement of Eq. (20.2.5) and is written as P(T t) ¼ FT (t) ¼ (1 elt )u(t) and the density function is fT (t) ¼ lelt u(t) Equation (20.2.5) will serve as the initial condition for the solution of PN {n, t}.
(20:2:6)
494
Classification of Random Processes
FIGURE 20.2.2
From Fig. 20.2.2 it can be observed that n events occurring in the interval (0, t þ Dt) can be partitioned as n events in (0, t) AND no event in (t, t þ Dt) OR (n 2 1) events in (0, t) AND 1 event in (t, t þ Dt). From assumptions 1 –4, we can write PN (n, t þ Dt) ¼ PN (n, t)½1 l Dt þ o(Dt) þ PN (n 1, t) l Dt þ o(Dt)
(20:2:7)
Rearranging terms in Eq. (20.2.7), dividing throughout by Dt, and taking the limit as Dt ! 0, we obtain a differential – difference equation PN (n, t þ Dt) PN (n, t) ¼ lim ½PN (n, t)l þ PN (n 1, t)l þ o(Dt) Dt!0 Dt!0 Dt lim
or dPN (n, t) ¼ lPN (n, t) þ lPN (n 1, t) dt
(20:2:8)
Equation (20.2.8) is a differential – difference equation and the two initial conditions for solving the are 1. PN (0, t) ¼ elt u(t) 2. PN (n, 0) ¼ 0, n . 0 Equation (20.2.8) can be solved recursively as follows: n ¼ 1:
dPN (1, t) ¼ lPN (1, t) þ lelt dt
n ¼ 2:
dPN (2, t) ¼ lPN (2, t) þ lPN (1, t) or dt
or PN (1, t) ¼ ltelt , PN (2, t) ¼
t0
(lt)2 lt e , 2
t0
Proceeding in a similar manner, we obtain the result for n events occurring in interval t as PN (n, t) ¼
ðlt)n lt e , n!
t0
(20:2:9)
The discrete-state continuous-time (DSCT) random process satisfying Eq. (20.2.9) is called a Poisson random process and has been shown in Fig. 20.1.2.
Solution of Eq. (20.2.7) Using Generating Functions
The differential –difference equation [Eq. (20.2.8)] can also be solved using generating functions. From Eq. (11.3.1) we can define the generating function for PN (n, t) as G(z, t) ¼
1 X n¼0
PN (n, t)zn
(20:2:10)
20.2
Poisson Process
495
Multiplying Eq. (20.2.8) by z n and summing over all n from 0 to 1, we have 1 1 1 X X dX PN (n, t)zn ¼ l PN (n, t)zn þ l PN (n 1, t)zn dt n¼0 n¼0 n¼0
or d G(z, t) ¼ l(z 1)G(z, t) dt
(20:2:11)
For a fixed z, Eq. (20.2.11) is an ordinary differential equation in t. The initial condition G(z, 0) is obtained as follows: G(z, 0) ¼
1 X
PN (n, 0)zn ¼ PN (0, 0) ¼ 1;
since PN (n, 0) ¼ 0 for n 1
n¼0
With this initial condition, the solution to Eq. (20.2.11) is G(z, t) ¼ elt(z1) ,
t0
(20:2:12)
The power-series expansion of Eq. (20.2.12) yields lt (lt)2 (ltÞ n þ þ zn þ G(z, t) ¼ elt 1 þ z þ z2 1! 2! n! Hence, PN(n, t) is the coefficient of zn in the preceding equation and is given by PN (n, t) ¼ P{N(t) ¼ n} ¼ elt
(lt)n , n!
t0
(20:2:13)
The statistics of the Poisson process as derived in Example 19.1.7 are Mean: E½N(t) ¼ mN (t) ¼ lt Variance: var½N(t) ¼ s2N (t) ¼ lt Autocorrelation: E½N(t1 )N(t2 ) ¼ RN (t1 , t2 ) ¼ l2 t1 t2 þ lmin (t1 , t2 ) Autocovariance: E{½N(t1 ) mN (t1 )½N(t2 ) mN (t2 )} ¼ CN (t1 , t2 ) ¼ lmin (t1 , t2 ) Normalized Autocovariance: CN (t1 , t2 Þ 1 ¼ min rN (t1 , t2 ) ¼ sN (t1 )sN ðt2 Þ l
rffiffiffiffi rffiffiffiffi t1 t2 , t2 t1
Since the mean is a function of time t and the autocorrelation is a function of t1 and t2, the Poisson process is a nonstationary process. Distribution of Interarrival Times
We will now find the distribution of interarrival times for the Poisson process N(t). Let T1 be the random time of occurrence of the first event after time t ¼ 0, T2 the random time
496
Classification of Random Processes
FIGURE 20.2.3
between the occurrence of the first and second events, and Tn the random time between the occurrence of the (n 2 1)st and nth events as shown in Fig. 20.2.3. The sequence of times fTi, i ¼ 1, . . . , ng is called interarrival times. Let {ti , i ¼ 1, . . . , n} be the realizations of these random interarrival times. Let Sn be the random variable representing the arrival time of the nth event or the waiting time until the nth event: S1 ¼ T1 , S2 ¼ T1 þ T2 , . . . , Sn ¼ T1 þ T2 þ þ Tn ¼
n X
Ti
(20:2:14)
i¼1
If {ti , i ¼ 1, . . . , n} are the realizations of the random arrival times {Si , i ¼ 1, . . . , n}, then t1 ¼ t1 , t2 ¼ t1 þ t2 , . . . , tn ¼ t1 þ t2 þ þ tn ¼
n X
ti
(20:2:15)
i¼1
We will find the distributions of these interarrival times {Ti } and the waiting time for the nth event Sn. The Poisson process can also be specified in terms of the arrival times {Si } as N(t) ¼
1 X
u(t Si )
(20:2:16a)
i¼1
where u(t) is a unit step function. Referring to Fig. 20.2.3, N(t) is the total number of events that have occurred in the time interval t, and each event causes an increment by 1. Hence Eq. (20.2.16a) can be simplified as N(t) ¼
1 X i¼1
u(t Si ) ¼
N(t) X i¼1
uðt Si Þ ¼
N(t) X
1,
t0
(20:2:16b)
i¼1
By definition, the first event {T1 . t} occurs after time t, and hence there are no events occurring in the interval (0, t). Hence from Eq. (20.2.5), we obtain P{T1 . t} ¼ P{N(t) ¼ 0} ¼ PN (0, t) ¼ elt u(t)
(20:2:17)
We will show that the interarrival times T1,T2, . . . , Tn are independent identically distributed (i.i.d.) random variables with density function elt u(t). The second event
20.2
Poisson Process
497
{T2 . t} occurs after the first event T1 ¼ t1: P{T2 . t j T1 ¼ t1 } ¼ P{T2 . t j S1 ¼ t1 } ¼ P ½no events in (t1 , t1 þ t) = S1 ¼ t1 (20:2:18) From the independence of nonoverlapping intervals (assumption 4) of the Poisson process, Eq. (20.2.18) can be written as P{T2 . t j S1 ¼ t1 } ¼ P{T2 . t} ¼ P{no events in (t1 , t1 þ t)} ¼ elt u(t)
(20:2:19)
Hence T1 and T2, are independent. In a similar manner, for the nth arrival time Tn, we can write P{Tn . t j Tn1 ¼ tn1 , . . . , T1 ¼ t1 } ¼ P{Tn . t j Sn1 ¼ tn1 , . . . , S1 ¼ t1 } (20:2:20) Again, from the independent increment property, Eq. (20.2.20) can simplified to P{Tn . t j Sn1 ¼ tn1 , . . . , S1 ¼ t1 } ¼ P{Tn . t} ¼ P{no events in (tn , t} ¼ elt u(t)
(20:2:21)
Thus the interarrival times T1, T2, . . . , Tn are i.i.d. random variables with FT (t) ¼ P(Tn t) ¼ (1 elt )u(t) and density function fT (t) ¼ lelt u(t). As a consequence, the probP ability density function for the waiting time for the nth event Sn ¼ ni¼1 Ti , n 1 is an n-fold convolution of lelt u(t), yielding an Erlang density with n-degrees of freedom similar to Eq. (7.4.1): fSn (t) ¼
(lt)n1 lelt , (n 1)!
l . 0, t . 0, n: integer . 0
(20:2:22)
Example 20.2.1 A message sending station sends two independent bitstreams X and Y with Poisson rates l and m. The receiving station receives the composite stream W ¼ X þ Y. We have to find (1) the rate of the composite stream and (2) the probability that stream Y arrives first. The density functions of the bitstreams X and Y are fX (x) ¼ elx
(lx)k (my)m u(x) and fY (y) ¼ emy u(y) k! m!
1. We will find the rate of W ¼ X þ Y using generating functions. The generating functions for X and Y are PX (z) ¼ elðz1Þ
and
PY (z) ¼ em(z1)
Since X and Y are independent, the pdf fW (w) is the convolution of fX(x) and fY (x), and the generating function PW (z) for fW (w) is given by the product PX(z)PY (z), or PW (z) ¼ e(lþm)(z1) . Hence fW (w) ¼ eðlþm)w
½(l þ m)wk u(w) k!
2. Let the times of arrival of streams X and Y be the random variables S and T. From Eq. (20.2.6) the interarrival times are exponentially distributed and hence fS (s) ¼ lels u(s) and
fT (t) ¼ memt u(t)
498
Classification of Random Processes
Since the streams are independent, the arrival times are also independent, and the joint density of the arrival times S and T is fST (s, t) ¼ lme(ltþms) ,
s 0, t 0
The event that stream Y arrives before stream X is the same as the arrival time of Y is before the arrival time of X. Hence P{Y X} ¼ P{T S}. We will find P(T S). The region {T S} is shown in Fig. 20.2.4: 1 ð1 ð1 ð1 lt e P(T S) ¼ lme(ltþms) dt ds ¼ lmems l 0 s 0 s ð1 m ¼m e(lþm)s ds ¼ lþm 0 Hence P(Y X) ¼
m lþm
Example 20.2.2 A Poisson process N(t) with rate l consists of a subprocess N1(t) with probability p and rate l1 and another subprocess N2(t) with probability (1 2 p) and rate l2. We have to find the probabilities P{N1 (t) ¼ n} and P{N2 (t) ¼ m} and their rates l1 and l2 . We are given the following: N(t) elt
(lt)k , k!
N1 (t) el1 t
(l1 t)k (l2 t)k , N2 (t) el2 t k! k!
From the statement of the problem N(t) ¼ N1 (t) þ N2 (t), and if N1 (t) ¼ n and N2 (t) ¼ m, then N(t) ¼ n þ m. The joint probability P{N1 (t) ¼ n, N2 (t) ¼ m} can be given in terms of conditional probabilities as P{N1 (t) ¼ n, N2 (t) ¼ m} ¼ P{½N1 (t) ¼ n, N2 (t) ¼ m j N(t) ¼ m þ n}elt
(lt)mþn (m þ n)!
where P{N(t) ¼ m þ n} ¼ elt (lt)mþn =½(m þ n)! has been substituted in the equation above. The event ½N1 (t) ¼ n, N2 (t) ¼ m j N(t) ¼ m þ n} can be considered as (m þ n)
FIGURE 20.2.4
20.2
Poisson Process
499
Bernoulli trials with success defined as N1 (t) ¼ n with probability p and failure as N2 (t) ¼ m with probability (1 2 p). Thus, using the binomial distribution [Eq. (4.2.2)], we have mþn n p (1 p)m P{½N1 (t) ¼ n, N2 (t) ¼ m j N(t) ¼ m þ n} ¼ n and P{N1 (t) ¼ n, N2 (t) ¼ mg can be given by P{N1 (t) ¼ n, N2 (t) ¼ m} ¼
(m þ n)! n (lt)mþn p (1 p)m elt n!m! (m þ n)!
¼ eltp elt(1p)
(ltp)n ½lt(1 p)m n! m!
The marginal distribution P{N1 (t) ¼ n} can be given as follows: P{N1 (t) ¼ n} ¼ eltp
1 (ltp)n X ½lt(1 p)m (ltp)n ¼ eltp elt(1p) n! m¼0 m! n!
Similarly P{N2 (t) ¼ m} ¼ elt(1p)
½lt(1 p)m m!
Hence, the Poisson rates for N1(t) and N2(t) are l1 ¼ lp and l2 ¼ l(1 2 p). Let S1n and S2m be the waiting times for the nth and mth events of the Poisson subprocesses N1(t) and N2(t) respectively. We will find the probabilities of (1) the waiting time for the first event in N1(t) is greater than the waiting time for the first event in N2(t), or P{S11 . S21 }, and (2) the waiting time for the second event in N1(t) is less than the waiting time for the first event in N2(t), or P{S12 , S21 }. From Eq. (20.2.14) the waiting times can be given in terms of the interarrival times, or S11 ¼ T11, S21 ¼ T21 and S12 ¼ T11 þ T12. 1. From Example 20.2.1 P{S11 . S21 } ¼ P{T11 . T21 } ¼
l1 lp ¼p ¼ l1 þ l2 lp þ l(1 pÞ
2. Since S11 , S12, we can write P{S12 , S21 } ¼ P{S12 , S21 ; S11 , S21 } or P{S12 , S21 } ¼ P{S12 , S21 jS11 , S21 }P{S11 , S21 } From Example 20.2.1 PfS11 , S21 g ¼ l 2 =ðl1 þ l2 Þ and PfS12 , S21 j S11 , S21 g ¼ PfT11 þ T12 , T21 j T11 , T21 g ¼ PfT12 , T21 g ¼
l2 l1 þ l2
Hence
PfS12
l2 , S12 g ¼ ¼ ð1 pÞ2 l1 þ l2
Example 20.2.3 Advance booking for a World Series baseball game is open on weekdays only from 10:00 a.m. to 3:00 p.m. During the lunch hour from 12:00 to 1:00 p.m.,
500
Classification of Random Processes
customers arrive at the rate of l ¼ 8 h21 (8 per hour) and for the rest of the time at the rate of l ¼ 4h21. The arrival time N(t) of customers to the booking counter is a random process given by P{N(t) ¼ k} ¼ eLt
(Lt)k k!
where the rate L is a binary random variable assuming values of 4 and 8 with probabilities 0.2 and 0.8 respectively. We have to find E[N(t)], var[N(t)] and determine the variance to mean ratio s2 =m. If this ratio equals 1, we can conclude that N(t) is Poisson. We note that the distribution of N(t) conditioned on either L¼8 or 4 is Poisson. Hence E½N(t) j L ¼ 8 ¼ 8 and E½N(t) j L ¼ 4 ¼ 4. This result is used in the following equation: E½N(t)j ¼ E½NðtÞ j L ¼ 8P(L ¼ 8) þ E½N(t) j L ¼ 4P(L ¼ 4) ¼ 8 0:2 þ 4 0:8 ¼ 1:6 þ 3:2 ¼ 4:8 In a similar manner, we can find the second moment E[N 2(t)] with conditional second moments E½N 2 (t) j L ¼ 8 ¼ l þ m2 ¼ 8 þ 64 ¼ 72 and E½N 2 (t) j L ¼ 4 ¼ 4 þ 16 ¼ 20. From the conditional second moments, E[N 2(t)] can be given as follows: E½N 2 (t) ¼ E½N 2 (t) j L ¼ 8P(L ¼ 8) þ E½N 2 (t) j L ¼ 4P(L ¼ 4) ¼ 72 0:2 þ 20 0:8 ¼ 30:4 Hence the variance of N(t) is given by var(N(t)) ¼ E½N 2 (t) {E½N(t)}2 ¼ 30:4 4:82 ¼ 7:36 The variance : mean ratio s2 =m ¼ 7:36=4:8 ¼ 1:53. Since this ratio is not equal to 1, N(t) is not a Poisson process. Example 20.2.4 Customers arrive at a checkout counter at a Poisson rate of l per minute. The service time is a random variable Y in minutes. We consider two cases: (1) Y is a constant ¼ k minutes and (2) Y is exponentially distributed with density function fY (y) ¼ memy u(y). For both cases we will find the probabilities that (1) a customer does not have to wait and (2) the mean value of a customer’s waiting time. From Eq. (20.2.6) the interarrival time T of customers is distributed as fT (t) ¼ lelt u(t), and from Eq. (20.2.19) the interarrival times are independently distributed. The service time Y is distributed as fY (y) ¼ memy u(y). If the interarrival time T is greater than the service time Y, then there will be no waiting time for the customer. 1. Service time is a constant: Y ¼ k minutes (a) Pfno waiting time for the customerg ¼ PfT . kg. Hence 1 ð1 lt le P{T . k} ¼ lelt dt ¼ ¼ elk l k k
(b) If T k, then the customer has to wait (k 2 T ) minutes and the average waiting time is given by ðk 1 E½k T ¼ (k t)lelt dt ¼ k (1 ekl ) l 0
20.2
Poisson Process
501
2. Service time Y exponentially distributed: fY (y) ¼ memy u(y) (a) Again, P fno waiting time for the customerg ¼ PfT . Yg and ð1 P{T . Y j Y ¼ t}fY (t)dt P{T . Y} ¼ 0
ð1 ¼
elt memt dt ¼
0
m lþm
(b) Here also, if T Y, then the customer has to wait (Y 2 T ) minutes. We have to find E[Y 2 T ]. In terms of conditional expectation, we can write ð1 E½Y T ¼ E½Y T j Y ¼ y fY (y)dy 0
and from the result 1b (above) the conditional expectation E½Y T j Y ¼ y is ðy 1 E½Y T j Y ¼ y ¼ (y t)lelt dt ¼ y (1 ely ) l 0 Substituting this equation into the previous equation, we obtain ð1 1 l ly E½Y T ¼ y (1 e ) m emy dy ¼ l m(l þ m) 0 Poisson White Noise
The Poisson process given by Eq. (20.2.16) can be further generalized by associating with each event T a random weight A. The weights are i.i.d. random variables with density function fA(a) and are also independent of the arrival times S. The weighted Poisson process X(t) is defined by X(t) ¼
1 X
Ai u(t Si ),
t0
(20:2:23a)
i¼1
which can be simplified from Eq. (20.2.16.b) as follows: X(t) ¼
1 X
Ai u(t Si ) ¼
i¼1
N(t) X
Ai u(t Si ) ¼
i¼1
N(t) X
Ai ,
t0
(20:2:23b)
i¼1
The pdf fX (x; t) can be expressed in terms of the conditional expectation as X fX ½x; tjN(t)P{N(t)} fX (x; t) ¼
(20:2:24)
N(t)
The conditional probability fX (x; t j N(t) ¼ i can be evaluated from Eqs. (20.2.23): i ¼ 1:
fX ½x; t j N(t) ¼ 1 ¼ fA (a)
(20:2:25)
i ¼ 2:
fX ½x; t j N(t) ¼ 2 ¼ fA (a1 þ a2 )
(20:2:26)
Since the Ai terms are i.i.d. random variables, fA (a1 þ a2 ) in Eq. (20.2.26) is a convolution of fA (a) with itself; or fX ½x; t j N(t) ¼ 2 ¼ fA (a) fA (a) Proceeding in this fashion, fX ½x; t j N(t) ¼ i is an i-fold convolution of fA (a) and fX ½x; t j N(t) ¼ i ¼ fA (a) fA (a) fA (a) ¼ fA(i) (a)
(20:2:27)
502
Classification of Random Processes
In Eq. (20.2.4), P{N(t) ¼ i} is a Poisson distribution given by Eq. (20.10.13): P{N(t) ¼ i} ¼ PN (i, t) ¼ elt
(lt)i i!
(20:2:28)
Substituting Eqs. (20.2.27) and (20.2.28) in Eq. (20.2.24) we obtain the pdf of X(t) as fX (x; t) ¼ elt
1 X
fA(i) (a)
i¼1
(lt)i i!
(20:2:29)
The mean and autocorrelation of the generalized Poisson process X(t) can be obtained from the properties of expectations as follows: Mean: E½X(t) ¼ E{E½X(t) j N(t) ¼ n} ( " #) N(t)¼n 1 N(t)¼n X X X ¼E E Ai E½Ai PN (n, t) ¼ n¼1
i¼1
¼
1 X
i¼1
1 X (lt) lt (lt)n e ¼ mA elt n! (n 1)! n¼1 n
mA n
n¼1
¼ mA ltelt ¼
1 X (lt)n m¼0
m!
¼ mA lt ¼ mX (t),
t0
(20:2:30)
Autocorrelation: E½X(t1 )X(t2 ) ¼ E{X(t1 )½X(t2 ) X(t1 ) þ X(t1 )} ¼ E½X 2 (t1 ) þ E½X(t1 )E½X(t2 ) X(t1 ) ¼ E{E½X 2 (t1 ) j N(t1 )} þ E½X(t1 ) j N(t1 )E½(X(t2 ) X(t1 )) j N(t2 t1 )
(20:2:31)
We will evaluate E{E½X 2 (t1 ) j N(t1 )}: ( ) n X n X 2 E½Ai A j E{E½X (t1 ) j N(t1 ) ¼ n} ¼ E i¼1 j¼1
¼E
8 > > n <X > > : i¼1
E½A2i þ
n X n X
E½Ai E½A j
i¼1 j¼1 i=j
9 > > = > > ;
¼ E{nE½A2 þ n(n 1)m2A } ¼
1 X
E½A2 nelt1
n¼1
¼ E½A2 lt1 elt1
1 (lt1 )n X (lt1 )n þ m2A n(n 1)elt1 n! n! n¼1
1 X (lt1 )m m¼0
m!
þ m2A l2 t12 elt1
1 X (lt1 )m m¼0
m!
¼ E½A2 lt1 þ m2A l2 t12 (20:2:32)
20.2
Poisson Process
503
Substituting Eqs. (20.2.30) and (20.3.32) in Eq. (20.2.31) and E½A2 ¼ s2A þ m2A , we obtain E½X(t1 )X(t2 ) ¼ E½A2 l(t1 ) þ m2A l2 t12 þ mA lt1 ½mA lt2 mA lt1 ¼ ½s2A þ m2A lt1 þ m2A l2 t1 t2 ¼ RX (t1 , t2 )
(20:2:33a)
Equation (20.2.33a) was obtained on the premise that t1 , t2. We can derive a similar equation for t2 , t1. Hence the autocorrelation function RX(t1, t2) is given by RX (t1 , t2 ) ¼ ½s2A þ m2A l min(t1 , t2 ) þ m2A l2 t1 t2 , t1 , t2 , 0
(20:2:33b)
The autocovariance function CX (t1, t2) is given by CX (t1 , t2 ) ¼ (s2A þ m2A )l min(t1 , t2 ), t1 , t2 0
(20:2:34)
Example 20.2.5 The events of a Poisson process with l ¼ 12 are weighted with i.i.d. random variables {Ai }, resulting in a weighted Poisson process X(t). The weights are uniformly distributed in the interval (0,2). We will find the mean, autocorrelation, and autocovariance of X(t). The mean value of the weights mA ¼ 1. The variance of the weights s2A ¼ 13. X(t) given by Eq. (20.2.23) is shown in Fig. 20.2.5. Mean. From Eq. (20.2.30) the mean value of X(t) is mX (t) ¼ mA lt ¼ 1 12 t ¼ t=2. Autocorrelation. The second moment of A is E½A2 ¼ s2A þ m2A ¼ 13 þ 12 ¼ 43. Hence, from Eq. (20.2.33), the autocorrelation function is given by 2 41 2 1 2 1 min(t1 , t2 ) þ 1 RX (t1 , t2 ) ¼ t1 t2 ¼ min(t1 , t2 ) þ t1 t2 32 2 3 4 Autocovariance. From Eq. (20.2.34) the ACF is CX (t1 , t2 ) ¼ 23 min(t1 , t2 ): Formal Derivative of Poisson Process
The generalized Poisson process given by Eq. (20.2.23) shown in Fig. 20.2.5 consists of a series of step functions occurring at Poisson-distributed random times with random
FIGURE 20.2.5
504
Classification of Random Processes
weights. The formal derivative of X(t) yields a Poisson impulse process V(t) consisting of a series of Dirac delta functions: 1 1 X d dX X(t) ¼ Ai u(t Si ) ¼ Ai d(t Si ), dt dt i¼1 i¼1
t0
(20:2:35)
The mean of V(t) can be calculated as mV (t) ¼
dmX (t) dmA lt ¼ ¼ mA l, dx dt
t0
(20:2:36)
The autocorrelation function of V(t) is RV (t1 , t2 ) ¼
@2 RX (t1 , t2 ) @2 ¼ {½s2A þ m2A l min(t1 , t2 ) þ m2A l2 t1 t2 } @t1 @t2 @t1 @t2
¼ ½s2A þ m2A ld(t2 t1 ) þ m2A l2 ¼ RV (t2 t1 ) ¼ RV (t)
(20:2:37)
Since mV is independent of time and RV (t1 , t2 ) is a function of the time difference t2 2 t1, we conclude that V(t) is a stationary process. The autocovariance function is CV (t1 , t2 ) ¼ ½s2A þ m2A ld(t2 t1 ) ¼ CV (t2 t1 ) ¼ CV (t)
(20:2:38)
The FT SV 0 (v) of the acf CV (t) of Eq. (20.2.38) is given by SV 0 (v) ¼ FT{½s2A þ m2A ld(t2 t1 )} ¼ l½s2A þ m2A
(20:2:39)
This is the psd of the process V 0 (t) ¼ V(t) mA l. Since the spectrum is constant over the entire frequency range, the process V 0 (t) is called Poisson white noise. Example 20.2.6 We will determine the derivative process V(t) of Example 20.2.5, where l ¼ 12 and the weight process A . 0 is uniformly distributed in the interval (0,2). The process V(t) is shown in Fig. 20.2.6. The mean mV is obtained from Eq. (20.2.36) as mV ¼ mA l ¼ 1 12 ¼ 12. The autocorrelation function from Eq. (20.2.37) is 4 1 RV (t) ¼ ½s2A þ m2A d(t2 t1 ) þ m2A l2 ¼ d(t) þ 3 4
FIGURE 20.2.6
20.3
Binomial Process
505
and the autocovariance from Eq. (20.2.38) is CV (t) ¼ 43 d(t). The psd equals FT½RV ðtÞ given by SV (v) ¼ 43 þ ðp=2Þd(v).
B 20.3
BINOMIAL PROCESS
Bernoulli Process
In Section 4.1 we discussed Bernoulli trials as repeated functionally and statistically i.i.d. trials, each of which consists of only two events s and f with probabilities p and q ¼ (1 2 p) respectively. Consider a countably infinite sequence {Xn , n ¼ 0, 1, . . .} of random variables defined by
1 for success s in the nth trial Xn ¼ 0 for failure f in the nth trial with probabilities P(Xn ¼ 1) ¼ p P(Xn ¼ 0) ¼ 1 p
(20:3:1)
Thus Xn represents a Bernoulli process, shown in Fig. 20.3.1. We will now find the statistics of this process: Mean: E½Xn ¼ mX ¼ 1 P(Xn ¼ 1) þ 0 P(XN ¼ 0) ¼ p
(20:3:2)
Variance: var½Xn ¼ s2X ¼ E½X 2 m2X ¼ 12 p þ 02 (1 p) p2 ¼ p(1 p)
(20:3:3)
Autocorrelation: ( E½Xn1 Xn2 ¼ RX (n1 , n2 ) ¼
12 p þ 02 (1 p) ¼ p, 2
E½Xn1 E½Xn2 ¼ p ,
n1 n2 ¼ 0
(20:3:4)
n1 n2 = 0
Autocovariance: CX (n1 ; n2 ) ¼ RX (n1 ; n2 ) m2X ( 12 p þ 02 (1 p) p2 ¼ p(1 p), ¼ E½Xn1 E½Xn2 ¼ p2 ¼ 0, n1 n2 = 0
FIGURE 20.3.1
n1 n2 ¼ 0
(20:3:5)
506
Classification of Random Processes
Normalized Autocovariance: rX (n1 , n2 ) ¼
1, 0,
n1 n2 ¼ 0 n1 n2 = 0
(20:3:6)
Since the mean is independent of time n and the autocorrelation depends only on the time difference (n1 – n2), the process Xn is stationary. Binomial Process
We now define a new process Yn of the sum of the random variables {Xi } in a Bernoulli process as discussed earlier. This is called the binomial process, given by Yn ¼
n X
Xi ,
n.0
(20:3:7)
i¼1
This process, corresponding to the Bernoulli process of Fig. 20.3.1, is shown in Fig. 20.3.2 and is a discrete-state discrete-time (DSDT) process. The probability of k successes in n trials has already been derived in Eq. (4.2.2) and is given by a binomial distribution: n k p (1 p)nk Probability{k successes in any sequence in n trials} ¼ P(Yn ¼ k) ¼ k The statistics of the binomial process are: Mean. Since the probability of each success is p, the mean value of Yn can be obtained using the linearity of the expectation operator: " # n n X X E ½Yn ¼ E Xi ¼ E ½Xi ¼ np, n . 0 (20:3:8) i¼1
i¼1
Variance. From the independence of Xi, the second moment of Yn can be given by " #2 n n n X n X X X 2 Xi ¼ E½Xi2 þ E ½Xi X j ¼ np þ n(n 1)p2 , n . 0 E½Yn ¼ E i¼1
s2Y
¼
E ½Yn2
i¼1
i¼1 j¼1
2
{E ½Yn } ¼ np þ n(n 1)p2 n2 p2 ¼ np(1 p),
n.0 (20:3:9)
FIGURE 20.3.2
20.4
Independent Increment Process
507
Autocorrelation: For n1 n2, we have " # n2 X n1 X E ½Yn1 Yn2 ¼ RY (n1 , n2 ) ¼ E Xi X j j¼1 i¼1
" ¼E
n1 X i¼1
# Xi2
þ
n1 X n1 X
E ½Xi X j þ
j¼1 i¼1 i=j
nX n1 2 n1 X
E ½Xi X j
j¼1 i¼1 i=j
¼ n1 p þ n1 (n1 1)p2 þ n1 (n2 n1 )p2 or RY (n1 , n2 ) ¼ n1 p(1 p) þ n1 n2 p2 ;
for
n1 n2
(20:3:10)
Similarly for n2 n1 we have RY (n1 , n2 ) ¼ n2 p(1 p) þ n1 n2 p2 ;
for
n2 n1
(20:3:11)
n1 , n2 . 0
(20:3:12)
Combining Eqs. (20.3.10) and (20.3.11), we obtain RY (n1 , n2 ) ¼ p(1 p) min(n1 , n2 ) þ n1 n2 p2 , Autocovariance: CY (n1 , n2 ) ¼ p(1 p) min (n1 , n2 ), n1 , n2 . 0
(20:3:13)
Unlike the Bernoulli process, the mean of the binomial process is dependent on time n and the autocorrelation function is dependent on both n1 and n2 and hence is a nonstationary process.
B 20.4
INDEPENDENT INCREMENT PROCESS
A random process X(t) is called an independent increment process if for a sequence of times t1 , t2 , . . . , tn the increments X(t1 ), {X(t2 ) X(t1 )}, . . . , {X(tn ) X(tn1 )} of the process X(t) are a sequence of independent random variables: E{½X(ti ) X(ti1 )½X(tiþ1 ) X(ti )} ¼ E ½X(ti ) X(ti1 ) E ½X(tiþ1 ) X(ti )
(20:4:1)
Further, if all order distributions of the increments fX(t) 2 X(s)g, t , s are dependent only on the time difference t ¼ t s, then X(t) is called a stationary independent increment process. We will show that a stationary independent increment process is not a stationary process, and its mean value and variance are given by E ½X(t) ¼ m0 þ m1 t
(20:4:2a)
where m0 ¼ E ½X(0) and m1 ¼ E ½X(1) E ½X(0) and var½X(t) ¼ s2X(t) ¼ s20 þ s21 t where s20 ¼ E ½X(0) m0 2 and s21 ¼ E ½X(1) m1 2 s20 .
(20:4:2b)
508
Classification of Random Processes
We will verify Eq. (20.4.2a). Defining g(t) ¼ E ½X(t) X(0), for any t and t, we have g(t þ t) ¼ E ½X(t þ t) X(0) ¼ E{½X(t þ t) X(t) þ ½X(t) X(0)} ¼ E ½X(t þ t) X(t) þ E ½X(t) X(0)
(20:4:3)
In Eq. (20.4.3) E ½X(t) X(0) ¼ g(t) by definition
(20:4:4)
and from the stationary independent increment property we can write E ½X(t þ t) X(t) ¼ E ½X(t) X(0) ¼ g(t)
(20:4:5)
Substituting Eqs. (20.4.5) and (20.4.4) in Eq. (20.4.3), we obtain g(t þ t) ¼ g(t) þ g(t)
(20:4:6)
Differentiating both sides of Eq. (20.4.6) independently with respect to t and t, we have d d g(t þ t) ¼ g(t); dt dt
d d g(t þ t) ¼ g(t) dt dt
(20:4:7)
Hence g0 (t) ¼ g0 (t) and in particular at t ¼ 1, g(t)jt¼1 ¼ E ½X(1) X(0) ¼ k1 . Substituting k1 for g0 (t) and solving the differential equation g0 (t) ¼ k1 , we obtain the solution g(t) ¼ k1t þ k0. Substitution of the initial condition g(0) ¼ E ½X(0) X(0) ¼ 0 in the solution results in g(t) ¼ k1t, or E ½X(t) X(0) ¼ E ½X(1) X(0)t E ½X(t) ¼ m0 þ m1 t
(20:4:8) (20:4:2a)
thus verifying Eq. (20.4.2a). In a similar manner we can verify Eq. (20.4.2b). Hence, a random process with stationary independent increments is nonstationary since the mean and variance are functions of time.
Uncorrelated and Orthogonal Increment Processes
A random process X(t) is called an uncorrelated increments process if for a sequence of times t1 , t2 , t3 , t4 the increments fX(t2) 2 X(t1)g and fX(t4) 2 X(t3)g of the process X(t) are uncorrelated random variables, or E{½X(t2 ) X(t1 )½X(t4 ) X(t3 )} ¼ E ½X(t2 ) X(t1 )E ½X(t4 ) X(t3 )
(20:4:9)
Similarly, the random process X(t) is called an orthogonal increments process if E{½X(t2 ) X(t1 )½X(t4 ) X(t3 )} ¼ 0
(20:4:10)
The term independent increment process implies an uncorrelated increment process, but the converse is not true. If a process has uncorrelated increments, it need not necessarily have independent increments. Also, if a process X(t) has uncorrelated increments, then the process Y(t) ¼ X(t) E ½X(t) has orthogonal increments. Example 20.4.1 (Poisson Process) A Poisson process N(t) with random arrival times fSk , k ¼ 1, . . . , n, . . .} and random interarrival times {Tk , k ¼ 1, . . . , n, . . .} is an
20.4
Independent Increment Process
509
independent increment process since P{Tk1 s, Tk t} ¼ P{½N(Sk1 ) N(Sk2 ) ¼ 1, ½N(Sk ) N(Sk1 ) ¼ 1} ¼ P ½N(Sk1 ) N(Sk2 ) ¼ 1 P ½N(Sk ) N(Sk1 ) ¼ 1 ¼ (1 elt )ð1 elt Þu(s)u(t) The independent increments are stationary since P ½N(t) N(s) ¼ 1 ¼ l(t s)el(ts) depends only on the time difference (t 2 s) and not the individual times t and s. The mean of the Poisson process can be obtained by substituting in Eq. (20.4.2a) m0 ¼ E ½N(0) ¼
1 X
k el:0
k¼0
(l:0)k ¼0 k!
and with
E½N(1) ¼
1 X
k el
k¼0
lk ¼l k!
m1 ¼ l 0 ¼ l and hence E½N(t) ¼ lt, a result that has been obtained previously. The variance of the process can be obtained by substituting in Eq. (20.4.2b) s20 ¼ 0 and s21 ¼
1 X
k2 el
k¼0
lk (lt)2 ¼ l k!
Example 20.4.2 (Binomial Process) Eq. (20.3.7) as Yn ¼
and hence var ½N(t) ¼ lt
A binomial process has been defined in
n X
Xi ,
n.0
i¼1
where n is the number of trials and Xi represents either 1 for success or 0 for failure at count i. Since each Xi is independent of Xj, j = i, we conclude that the process Yn is an independent increment process. The independent increments are stationary because k nm k P ½(Yn Ym ¼ k) ¼ p (1 p)(nm) , n . m k is dependent only on the count difference (n 2 m) and not on individual counts n and m. In the binomial process m0 ¼ E½Y0 ¼ 0 by definition and E ½Y1 ¼ E ½X1 ¼ p. Substitution of E ½Y1 in m1 results in m1 ¼ p 0 ¼ p, and hence, substituting m0 ¼ 0, m1 ¼ p, and t ¼ n in Eq. (20.4.2a), we obtain E ½Yn ¼ np, a result obtained previously. In a similar manner the variance of the process Yn can be obtained by substituting in Eq. (20.4.2b) s20 and s21 ¼ p(1 p), resulting in var ½Yn ¼ npð1 pÞ, another result obtained previously. Elementary Random-Walk Process
The Bernoulli process considered earlier is now modified as shown in Fig. 20.4.1 so that the failure is now represented by 21 instead of 0. Under this modification we define a random variable Zn Zn ¼
1 1
for success in the nth trial for failure in the nth trial
(20:4:11a)
510
Classification of Random Processes
FIGURE 20.4.1
with probabilities P(Zn ¼ 1) ¼ p P(Zn ¼ 1) ¼ 1 p ¼ q
(20:4:11:b)
If p ¼ 12, this random process is called a symmetric random walk. The mean and variance of Zn are Mean: mZ ¼ E ½Zn ¼ 1 P(Xn ¼ 1) 1 P(XN ¼ 1) ¼ p (1 p) ¼ 2p 1 (20:4:12) Second Moment: E ½Zn2 ¼ 1 p þ 1 (1 p) ¼ 1
(20:4:13)
Variance: s2Z ¼ var½Zn ¼ 12 p þ (1)2 (1 p) (2p 1)2 ¼ 4p(1 p)
(20:4:14)
In much the same way as we did in Eq. (20.3.7), we define a new process Wn as the sum of the random variables fZig in the modified Bernoulli process. This is called an elementary random-walk process, given by Wn ¼
n X
Zi ,
n.0
(20:4:15)
i¼1
This process, shown in Fig. 20.4.2, is also a discrete-state discrete-time (DSDT) process. The probability that there are k successes and (n 2 k) failures in n trials is again a binomial
FIGURE 20.4.2
20.4
Independent Increment Process
511
distribution given by n k Probability{k successes in any sequence in n trials} ¼ P(Wn ¼ k) ¼ p (1 p)nk k The statistics of this elementary random walk are Mean: " mW ¼ E ½Wn ¼ E
n X
# Zi ¼
i¼1
n X
E ½Zi ¼ n(2p 1),
n.0
(20:4:16)
i¼1
Variance. From the independence of Zi, the second moment of Wn can be given by " #2 n n n X n X X X 2 E ½Wn ¼ E Zi ¼ E ½Zi2 þ E ½Zi Z j i¼1
i¼1
i¼1 j¼1 2
¼ n þ n(n 1)(2p 1)
n.0
Hence s2W ¼ E ½Wn2 {E ½Wn }2 ¼ n þ n(n 1)(2p 1)2 n2 (2p 1)2 ¼ 4np(1 p),
n.0
(20:4:17)
Autocorrelation. For n1 n2, we obtain " # n2 X n1 X Zi Z j E ½Wn1 Wn2 ¼ RW (n1 , n2 ) ¼ E j¼1 i¼1
" ¼E
n1 X i¼1
# Zi2 þ
n1 X n1 X j¼1 j¼1 i=j
E ½Zi Z j þ
nX n1 2 n1 X
E ½Zi Z j
(20:4:18)
j¼1 i¼1 i=j
Substituting for the expectation operators from Eqs. (20.4.12) and (20.4.13), we obtain E ½Wn1 Wn2 ¼ 4p(1 p)n1 þ n1 n2 (2p 1)2 with a similar expression for n2 n1 given by E ½Wn1 Wn2 ¼ 4p(1 p)n2 þ n1 n2 (2p 1)2 . Combining these two equations, we have E ½Wn1 Wn2 ¼ RW (n1 , n2 Þ ¼ 4p(1 p) min(n1 , n2 ) þ n1 n2 (2p 1)2
(20:4:19)
Autocovariance: CW (n1 , n2 ) ¼ 4p(1 p) min(n1 , n2 )
(20:4:20)
Since the mean is a function of time n and the autocorrelation is a function of both n1 and n2, we conclude that the random-walk process is also a nonstationary process. Example 20.4.3 (Random-Walk Process) Since the random-walk process is a takeoff from the Bernoulli process and with distribution function similar to the binomial process, we can conclude that this process is also a stationary independent increment process. We will show that the random-walk process satisfies Eqs. (20.4.2a) and (20.4.2b). In this process m0 ¼ E ½W0 ¼ 0 by definition and
512
Classification of Random Processes
m1 ¼ E ½W1 ¼ E ½Z1 ¼ 2p 1. Substituting for m0 ¼ 0, m1 ¼ (2p 2 1) and t ¼ n in Eq. (20.4.2a), we obtain E ½Wn ¼ mW ¼ n(2p 1), which is the same as Eq. (20.4.16). In a similar manner, s20 ¼ E ½W0 m0 2 ¼ 0 and s21 ¼ E ½W12 m21 ¼ 1 (2p 1)2 ¼ 4p(1 p). Hence var½Wn ¼ 4np(1 p), which is the same as Eq. (20.4.17).
B 20.5
RANDOM-WALK PROCESS
An elementary random walk has been discussed in the previous section. We will generalize this concept a little further. A particle is initially at rest at i ¼ 0 with W0 ¼ 0. At times fi ¼ 1,2, . . . , n, . . .g it undergoes jumps fZi, i ¼ 1, 2, . . . , n, . . .g, where fZig is a sequence of independent identically distributed random variables with probabilities 9 P(Zi ¼ þ1Þ ¼ p = P(Zi ¼ 1Þ ¼ q (20:5:1) i ¼ 1, . . . , n, . . . , P(Zi Z j ) ¼ P(Zi )P(Z j ), i = j ; P(Zi ¼ 0Þ ¼ 1 p q If Wn is the position of the particle after n jumps, then it can be written in terms of Zi as Wn ¼
n X
Zi , n . 0, and W0 ¼ 0
(20:5:2)
i¼1
The random processes Zi and Wn are shown in Fig. 20.5.1. If the particle tends to move indefinitely in either the positive or negative direction, then this random walk is called unrestricted. On the other hand, the particle may be restricted to þa in the positive direction and 2b in the negative direction where a and b are positive, in which case a and b are called absorbing barriers. Let the position of the particle at time n be equal to k where k ¼ 0, +1, +2, . . . , +n. We have to find the probability P(Wn ¼ kg. We observe that to reach point k, the particle undergoes r1 positive jumps, r2 negative jumps, and r3 zero jumps with the following constraining conditions: r1 þ r2 þ r3 ¼ n,
k ¼ r1 r2 ,
FIGURE 20.5.1
r3 ¼ n r1 r2
(20:5:3)
20.5 Random-Walk Process
513
With r1 as an independent variable P(Wnr1 ¼ k} is a multinomial distribution given by P(Wnr1 ¼ k) ¼
n! pr1 qr2 (1 p q)r3 r1 !r2 !r3 !
(20:5:4)
Substituting the constraints of Eq. (20.5.3) in Eq. (20.5.4), we obtain P(Wnr1 ¼ k) ¼
n! pr1 qr1 k (1 p q)n2r1 þk r1 !(r1 k)!(n 2r1 þ k)!
(20:5:5)
From Eq. (20.5.5), the constraints for r1 are r1 k and r1 ðn þ kÞ=2. In other words, nþk kr (20:5:6) 2
where n þ2 k represents the integer closest to but not exceeding ðn þ kÞ=2. Since the events {Wnr1 ¼ k} for each r1 are mutually exclusive, we can sum over all possible values of r1 in Eq. (20.5.6) and write P(Wn ¼ kg as follows: X P(Wnr1 ¼ k) P(Wn ¼ k) ¼ r1
¼
bðnþkÞ=2c X r1 ¼k
n! r1 !(r1 k)!(n 2r1 þ k)!
pr1 qr1 k (1 p q)n2r1 þk
(20:5:7)
The probability P(Wn ¼ k) can be more easily determined by using the generating function (GF) defined in Eq. (11.3.1). The generating function for the jumps Zi is X zk P(Zi ¼ k) ¼ pz þ qz1 þ (1 p q)z0 PZ (z) ¼ E ½zZi ¼ k
¼
pz2 þ (1 p q)z þ q z
(20:5:8)
Since Wn is the sum of n independent jumps, the GF for Wn is the n-fold multiplication of the GF PZ (z), or 2 n pz þ (1 p q)z þ q n Wn , n.0 (20:5:9) PWn (z) ¼ E ½z ¼ ½PZ (z) ¼ z and since W0 ¼ 0, the GF for W0 can be defined as follows: PW0 (z) ¼ E ½zW0 ¼ ½PZ (z)0 ¼ 1
(20:5:10)
With PW0 (z) ¼ 1 from Eq. (20.5.10), we can take the s transform of PWn (z) ¼ ½PZ (z)n and write G(z, s) ¼
1 X
sn ½PZ (z)n ¼
n¼0
¼
z
s( pz2
1 1 sPZ (z)
z þ (1 p q)z þ q)
for
jsPZ (z)j , 1
The probability P(Wn ¼ k) is the coefficient of s nz k in Eq. (20.5.11).
(20:5:11)
514
Classification of Random Processes 3 Example 20.5.1 In a random-walk process p ¼ 12 and q ¼ 10 . We will determine the probability P(Wn ¼ k) of the particle being at k ¼ 3 in n ¼ 10 trials. We will solve this problem using the direct approach of Eq. (20.5.5) and the generating function approach of Eq. (20.5.11).
Direct Approach. In Eq. (20.5.5) the known quantities are n ¼ 10, k ¼ 3, p ¼ 12, and 3 q ¼ 10 . We have to find P(W10 ¼ 3) for all possible values of r1 the positive jumps. The values of r1 from Eq. (20.5.6) are 3,4,5,6. From Eq. (20.5.7) P(W10 ¼ 3) is given by P(W10 ¼ 3) ¼
6 X
10! 1r1 3 r1 3 1132r1 r !(r 3)!(13 2r1 )! 2 10 5 r ¼3 1 1 1
or P(W10 ¼ 3) ¼ 0:000192 þ 0:00756 þ 0:0567 þ 0:070875 ¼ 0:135327: 3 Generating Function Approach. Substituting the values of n ¼ 10, p ¼ 12, and q ¼ 10 in Eq. (20.5.11), we obtain z 1 ¼ G(z, s) ¼ 1 2 1 3 s(5z2 þ 2z þ 3) zs z þ zþ 1 2 5 10 10z
The required probability P(W10 ¼ 3) is the coefficient of s 10z 3 in the power-series expansion of G(z, s). The power-series expansion for G(z,s) in terms of s is given by k 1 X s(5z2 þ 2z þ 3) G(z, s) ¼ 10z k¼0 and the coefficient of s 10 is Coefficient of s10 ¼
2 10 5z þ 2z þ 3 10z
This coefficient of s 10 will be expanded as a power series in z. Expanding the preceding equation, the coefficient of z 3 is 135,327 Coefficient of z3 ¼ 1,000,000 135, 327 Hence, P(W10 ¼ 3) ¼ 1,000,000 , agreeing with the previous result obtained from the direct method.
Random Walk with Two Absorbing Barriers a and 2b
We will now consider a random walk with two absorbing barriers. The particle starts at an initial value of j and terminates at either a or 2b with a,b . 0. The initial value of the random walk is W0 ¼ j, b , j , a It can be shown from heuristic arguments that as n ! 1, the ultimate probability of absorption at either a or 2b is certain. We will define the probability of absorption as Pa (n, j) ¼ P{absorption at a at time n starting from j},
n ¼ 1, 2; . . .
(20:5:12)
20.5 Random-Walk Process
515
The probability Pa(n, j ) also represents the first passage of the particle into a at time n starting from j, without having entered either 2b or a at any previous times: Pa (n, j) ¼ P{b , W1 , a, . . . , b , Wn1 , a, Wn ¼ a j W0 ¼ j},
n ¼ 1, 2, . . .
(20:5:13)
At n ¼ 0, we have the initial probabilities 1, if j ¼ a (absorption occurs at a at 0 when it starts at a Pa (0, j) ¼ 0, if j = a (absorption cannot occurs at a at 0 not starting at a (20:5:14) and at j ¼ a and j ¼ 2b the end probabilities for n ¼ 1, 2, . . . , are Pa (n, a) ¼ 0, Pa (n, b) ¼ 0,
n ¼ 1, 2, . . . n ¼ 0, 1, 2, . . .
(20:5:15)
We define for convenience an event An as An ¼ {absorption at a at time n} and P{An jW0 ¼ j} ¼ Pa ðn, j)
(20:5:16)
Starting from j at n ¼ 0, after the first step at n ¼ 1, absorption at a at time (n 2 1) will occur for the cases when the state can be at either ( j þ 1) with probability p or ( j 2 1) with probability q, or remain at j with probability (1 2 p 2 q). Since these are mutually exclusive events, we have P{An j W0 ¼ j} ¼ pP{An1 j W0 ¼ j þ 1} þ qP{An1 j W0 ¼ j 1} þ (1 p q)P{An1 j W0 ¼ j}
(20:5:17)
Substituting the definition of Pa(n, j ) from Eq. (20.5.16) in Eq. (20.5.17), we obtain Pa (n, j) ¼ pPa (n 1, j þ 1) þ qPa (n 1), j 1) þ (1 p q)Pa (n 1, j), b , j , a,
n ¼ 1, 2, . . .
(20:5:18)
Equation (20.5.15) is a difference equation in the two variables n and j. We can eliminate the time variable n by taking the z transform of Pa(n, j ), or Ga (z, j) ¼
1 X
zn Pa (n, j)
(20:5:19)
n¼0
with the initial probability Pa(0, j ) given by Eq. (20.5.14). Multiplying Eq. (20.5.18) by z n and summing over all n, we obtain Ga (z, j) ¼ z{pGa (z, j þ 1) þ qGa (z, j 1) þ (1 p q)Ga (z, j)}
(20:5:20)
Equation (20.5.20) is a second-order difference equation in j. The end conditions from Eqs. (20.5.14) and (20.5.15) are Ga (z, a) ¼ 1,
Gb (z, a) ¼ 0
(20:5:21)
We can take another z transform for the variable j. Since j is bounded by a, we can solve the difference equation [Eq. (20.5.20)] more easily by assuming a solution of the form Ga(z, j ) ¼ lj(z). Thus l j (z) þ z pl jþ1 (z) þ ql j1 (z) þ (1 p q)l j (z) ¼ 0
516
Classification of Random Processes
or z pl2 (z) ½1 z(1 p q)l(z) þ zq ¼ 0 The solutions to the quadratic equation [Eq. (20.5.22)] are pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 z(1 p q) þ ½1 z(1 p q)2 4z2 pq l1 (z) ¼ 2zp pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 z(1 p q) ½1 z(1 p q)2 4z2 pq l2 (z) ¼ 2zp
(20:5:22)
(20:5:23)
with the relationship between the roots l1 (z)l2 (z) ¼ (zq=zp) ¼ (q=p) and l1 (z) . l2 (z). We will assume that z is positive and real. If l1(z) and l2(z) are to be real, then the discriminant in Eq. (20.5.23) must be positive. Hence we have the following inequality ½1 z(1 p q)2 4z2 pq . 0 or z2 ½(1 p q)2 4pq 2z(1 p q) þ 1 . 0
(20:5:24)
The factors of the preceding quadratic equation in z are pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2(1 p q) + 4(1 p q)2 4½(1 p q)2 4pq z1 ; z2 ¼ 2½(1 p q)2 4pq pffiffiffiffiffi 1 p q + 2 pq 1 1 ¼ ¼ pffiffiffiffiffiffi ¼ pffiffiffiffi pffiffiffi (1 p q)2 4pq 1 p q + 2 pq 1 ( p + q)2 or "
1 z1 pffiffiffiffi pffiffiffi 2 1 ( p q)
#"
# 1 z2 .0 pffiffiffiffi pffiffiffi 1 ( p þ q) 2
(20:5:25)
If z is positive, the inequality of Eq. (20.5.25) will be satisfied only when both 1 1 pffiffiffiffi pffiffiffi 2 and z2 , pffiffiffiffi pffiffiffi 2 1 ( p q) 1 ( p þ q) pffiffiffi pffiffiffi 2 pffiffiffi pffiffiffi 2 Since ( p þ q) . ( p q) , Eq. (20.2.25) is equivalent to z1 ,
0,z,
1 pffiffiffiffi pffiffiffi 1 ( p þ q)2
The general solution of the difference Eq. (20.5.2) can be given by Ga (z, j) ¼ c1 (z)lj1 (z) þ c2 (z)lj2 (z)
(20:5:26)
where c1(z) and c2(z) are arbitrary functions of z. Substituting the initial conditions of Eq. (20.5.21) in Eq. (20.5.26), we can solve for c1(z) and c2(z) as follows: c1 (z) ¼
lb1 (z)lb 2 (z) a b l2 (z) l2 (z)laþb 1 (z)
and
c2 ðzÞ ¼
1 la2 (z)
aþb lb 2 (z)l1 (z)
(20:5:27)
20.5 Random-Walk Process
517
Substituting Eq. (20.5.27) into Eq. (20.5.36), we obtain the solution Ga(z, j ) as Ga (z, j) ¼
j j lb1 (z)lb l1jþb (z) l2jþb (z) 2 (z)l1 (z) þ l2 (z) ¼ aþb a b aþb l2 (z) l2 (z)l1 (z) l1 (z) laþb 2 (z)
(20:5:28)
To obtain the probability of absorption at a at time n starting from j, we have to express Eq. (20.5.8) as a power series in z, and the coefficient of z n will yield Pa(n,j ). These involved calculations can be found in Refs. 13 and 17. However, the events of eventual absorption at a or – b are mutually exclusive, and their probabilities can be calculated easily. We will find the probability of eventual absorption at a starting from j. Equation (20.5.28) is rewritten as Ga (z, j) ¼
l1jþb (z) l2jþb (z) aþb laþb 1 (z) l2 (z)
(20:5:29)
The probability of eventual absorption at a starting at j will be given by the sum of the probabilities Pa(n, j ) over all n and can be obtained by substituting z ¼ 1 in Eq. (20.5.29): Pa ( j) ¼ P{absorption at a starting at j} ¼
1 X
Pa (n, j)
n¼0
¼ Ga (1, j) ¼
l1jþb (1) l2jþb (1) aþb laþb 1 (1) l2 (1)
(20:5:30)
Substituting z ¼ 1 in the roots l1(z) and l2(z), since l1(z) . l2(z), we have p , q, l1 (1) ¼
( p þ q) ( p q) q ( p þ q) þ ( p q) ¼ . l2 (1) ¼ ¼1 2p p 2p
p . q, l1 (1) ¼
( p þ q) þ ( p q) ( p þ q) ( p q) q ¼ 1 . l2 (1) ¼ ¼ 2p 2p p
(20:5:31)
p ¼ q, l1 (1) ¼ l2 (1) ¼ 1 Substituting Eq. (20.5.31) in Eq. (20.5.30), we obtain Pa ( j) ¼ G(1, j) ¼ paj
p jþb q jþb , paþb qaþb
p=q
(20:5:32a)
Using l’Hoˆpital’s rule when p ¼ q, we have Pa (j) ¼
jþb , aþb
p¼q
(20:5:32b)
In a similar manner we can obtain the solution G2b(z, j ) for the absorption at 2b starting from j. The result is Gb (z, j) ¼
l1ja (z) (aþb) l1 (z)
l2ja (z) l(aþb) (z) 2
The probability of absorption at 2b starting at j is given by Gb (1, j) ¼ Pb ( j) ¼ 1 Pa ( j) ¼ 1 Ga (1, j)
(20:5:33)
518
Classification of Random Processes
or 8 paj qaj > > < qbþj aþb , p qaþb Pb (0) ¼ Gb (1, 0) > aj > : , p¼q aþb
p=q (20:5:34)
Example 20.5.2 (Gambler’s Ruin) This is a classic problem that has wide applications in testing the efficacy of drugs, insurance payoffs, and so on. Two gamblers A and B wager on the flip of a biased coin. If heads show up, A wins 1 dollar from B, and if tails show up, A loses one dollar to B. The probability of heads is p and tails q ¼ 1 2 p. Initially A starts with k dollars and B with N 2 k dollars. The game stops when either A loses all the money or is ruined or A wins all the money and B is ruined. We want to calculate the probabilities of A either winning all the money or losing all the money. This problem can be cast in the random-walk framework with absorbing barriers. We will solve this problem from fundamentals and show that we get the same result from using generating functions. Each toss is an i.i.d. random variable Zi with probabilities
P(Zi ¼ þ1) ¼ p i ¼ 1, . . . , n, . . . , P(Zi Zj ) ¼ P(Zi )P(Zj ), i = j P(Zi ¼ 1) ¼ q ¼ (1 p) and the cumulative sum Wn is given by Eq. (20.5.2) with initial value W0 ¼ k. Or, Wn ¼
n X
Zi , n . 0
and
W0 ¼ k
(20:5:35)
i¼1
The game terminates when Wn ¼ N or Wn ¼ 0. The absorbing barriers are either N or 0. We define an event An as absorption at N at time n and the probability of absorption at N at time n on condition that the starting state is k, as follows: P{An jW0 ¼ k} ¼ PN (n, k) Starting from k at n ¼ 0, after the first step at n ¼ 1, absorption at N at time (n 2 1) will occur for the cases when the state can be at either (k þ 1) with probability p or (k 2 1) with probability q. Since these are mutually exclusive events, we have P{An jW0 ¼ k} ¼ pP{An1 jW0 ¼ k þ 1} þ qP{An1 jW0 ¼ k 1} and in terms of the defined probability PN(n, k) PN (n, k) ¼ pPN (n 1, k þ 1) þ qPN (n 1, k 1),
k ¼ 1, 2, . . . , N 1
with end conditions PN(n, N ) ¼ 1 and PN(n, 0) ¼ 0, for n ¼ 1, 2, . . . , N 2 1. We are interested in the eventual probability of absorption in N and hence we can eliminate n from the equation above and write PN (k) ¼ pPN (k þ 1) þ qPN (k 1),
k ¼ 1, 2, . . . , N 1
with end conditions PN(N ) ¼ 1 and PN(0) ¼ 0, Since p þ q ¼ 1, the preceding equation can be rewritten as PN (k)( p þ q) ¼ pPN (k þ 1) þ qPN (k 1)
20.5 Random-Walk Process
519
and rearranging terms, we have q PN (k þ 1) PN (k) ¼ ½PN (k) PN (k 1), p
k ¼ 1, 2, . . . , N 1
We solve this equation recursively as follows from the initial condition PN(0) ¼ 0 k ¼ 1: k ¼ 2:
q q PN (2) PN (1) ¼ ½PN (1) PN (0) ¼ PN (1) p p 2 q q PN (1) PN (3) PN (2) ¼ ½PN (2) PN (1) ¼ p p
k ¼ k 1:
q PN (k) PN (k 1) ¼ ½PN (k 1) PN (k 2) p k1 q ¼ PN (1) p
k ¼ N 1:
q PN (N) PN (N 1) ¼ ½PN (N 1) PN (N 2) p N1 q PN (1) ¼ p
Adding the first k21 equations, we obtain "
2 k1 # q q q þ þ PN (k) PN (1) ¼ PN (1) þ p p p or "
2 k1 # q q q PN (k) ¼ PN (1) 1 þ þ þ þ p p p k q 1 p ¼ PN (1) q , 1 p PN (k) ¼ PN (1)k,
p=q
p¼q
Substituting k ¼ N in the equation above, we can obtain PN(1) by applying the boundary condition PN(N ) ¼ 1 as shown below: q 1 p PN (1) ¼ N , p = q q 1 p
and
PN (1) ¼
1 , p¼q N
520
Classification of Random Processes
Substituting for PN(1) in the equation for PN(k), we obtain k k q q q 1 1 1 p p p PN (k) ¼ N ¼ N , q q q 1 1 p 1 p p ¼
k , N
p¼q
p=q
(20:5:36)
PN(k) is the probability that A starting from k dollars will eventually reach N dollars. The probability of ruin P0(k) of A is Nk p 1 q P0 (k) ¼ 1 PN (k) ¼ N , p = q p 1 q ¼
Nk , N
p¼q
(20:5:37)
The probability of ruin P0(k) can be easily obtained from Eq. (20.5.34) by substituting for the absorbing barriers b ¼ 0, a ¼ N and j ¼ k, which results in Eq. (20.5.37). Discussion of Results. We will investigate what happens when A plays against an infinitely rich adversary B. If p . q, then ðq=pÞ , 1, and as N ! 1 in Eq. (20.5.36), we have k q k 1 q p p . q: lim PN (k) ¼ lim ¼ 1 .0 (20:5:38) N N!1 N!1 p q 1 p This results in a finite probability that A will get infinitely rich. On the other hand, if p q, then (q=p) . 1, and as N ! 1 in Eq. (20.5.36), we have k q Nk 1 p p p , q: lim PN (k) ¼ lim ¼ lim ! 0 N N!1 N!1 N!1 q q (20:5:39) 1 p p ¼ q:
lim PN (k) ¼ lim
N!1
k
N!1 N
! 0
This results in A getting ruined with probability 1 if p q. The interesting part is that even if it is a fair game with equal probability of winning, a rich player will always ruin a poorer player. On the other hand, if p . q, A can get infinitely rich even against an infinitely rich adversary. The gambling halls all over the world make sure that the probability of any player winning is always less than 0.5! Example 20.5.3 An insurance company earns $1 per day from premiums with probability p. However, on each day it pays out a claim of $2 each day independent of the past with probability q ¼ 1 2 p. When a claim is paid, $2 is removed from the reserve
20.6
Gaussian Process
521
of money of $k. Hence we can assume that on the ith day, the income for that day is a random variable Zi equal to 1 with probability p and 21 with probability q as in Example 20.5.2. The cumulative income for the company is Wn as in Eq. (20.5.35). If it starts initially with a reserve capital of $k, then we have to find the probability that it will eventually run out of money. The solution is a reformulation of Eqs. (20.5.38) and (20.5.39). If p . q, then the probability that it will not run out of money is given by 1 (q=p)k . 0. If k is large, then it has very high probability of staying in business. On the other hand, if p q, it will always run out of money as indicated in Eq. (20.5.39).
B 20.6
GAUSSIAN PROCESS
Many of the physically occurring random phenomena can be modeled as a Gaussian process. Further, according to the central limit theorem, random variables characterized individually by arbitrary distributions can be modeled as Gaussian if a sufficient number of them can be added. The distribution of a more commonly used white noise is Gaussian. We have already stated (in Section 6.4) that a random variable X is Gaussiandistributed if its density function is given by fX (x) ¼
2 1 pffiffiffiffiffiffi eð1=2Þ½(xmX )=sX sX 2p
(20:6:1)
where mX is the mean value and s2X is the variance. A second-order random process X(t) is a Gaussian random process if for any collection of times ft1, t2, . . . , tng, the random variables, X1 ¼ X(t1), X2 ¼ X(t2), . . . , Xn ¼ X(tn) are jointly Gaussian distributed random variables for all n with the joint density given by fX1 X2 ...Xn (x1 , x2 , . . . , xn ) ¼
T 1 1 e(1=2)(xmX ) CX (xmX ) (2p)n=2 jCX j
where the mean vector mX and the covariance 2 2 3 mX (t1 ) CX (t1 ) 6 CX (t2 , t1 ) 6 mX (t2 ) 7 6 6 7 mX ¼ 6 . 7; CX ¼ 6 .. 4 4 .. 5 . CX (tn , t1 ) mX (tn )
(20:6:2)
matrix CX are given by CX (t1 , t2 ) CX (t2 ) .. . CX (tn , t2 )
3 CX (t1 , tn ) CX (t2 , tn ) 7 7 7 .. 5 . CX (tn )
(20:6:3)
Properties 1. A Gaussian process is completely determined if the mean vector mX and the covariance matrix CX are known. 2. Sum of Gaussian processes are also Gaussian. 3. If a Gaussian process is weakly stationary, it is also strictly stationary.
White Gaussian Noise
A white-noise process has been defined in Eq. (19.2.17) as a stationary process whose autocorrelation is a Dirac delta function. The white-noise process that is widely used for
522
Classification of Random Processes
FIGURE 20.6.1
modeling communication problems is the Gaussian process. A standard Gaussian whitenoise process V(t) has zero mean and unit variance with probability density 1 2 fV (v) ¼ pffiffiffiffiffiffi e(v =2) , 1 , v , 1 2p
(20:6:4)
The autocorrelation function and power spectral densities are given by RV (t) ¼ d(t);
SV (v) ¼ 1, 1 , v , 1
(20:6:5)
Similarly, a standard white Gaussian noise sequence fVng has zero mean and unit variance with the sequences coming from a density function given by Eq. (20.6.4). However, its autocorrelation function and power spectral densities are given by RV (h) ¼ dh ;
SV (v) ¼ 1, 1 , v , 1
(20:6:6)
In this study, 10,000 points of a Gaussian white noise fVng was generated from a computer using the Box – Mueller method. The noise sequence was standardized to zero mean and unit variance. This sequence is shown in Fig. 20.6.1a for only 200 points for clarity, and its histogram illustrating the Gaussian nature of the density is shown in Fig. 20.6.1b. Example 20.6.1 [AR(1) Process] random process Xi was given by
In Example 19.2.11 a discrete zero mean stationary
Xn ¼ fXn1 þ Vn , n ¼ 1, . . . , X0 ¼ V0 where fVn, n ¼ 0, 1. . .g is a Gaussian process. This process is called an autoregressive (1) process. We will investigate this process assuming that Vn is standard Gaussian white noise and the coefficient f ¼ 0.8. We will show that Xn is also Gaussian-distributed. By defining B k as a backward difference operator defined by Bk Xn ¼ Xnk , k 0, we can write the given equation as (1 fB)Xn ¼ Vn or Xn ¼ (1 fB)1 Vn ¼ (1 þ fB þ f2 B2 þ þ fk1 Bk1 )Vn ¼ Vn þ fVn1 þ f2 Vn2 þ þ fk1 Vnkþ1 þ Since Xn is a sum of a weighted Gaussian-distributed random sequence, it is also a Gaussian-distributed continuous-state discrete-time (CSDT) random process. The
20.7
Wiener Process (Brownian Motion)
523
FIGURE 20.6.2
variance of this process as given by Eq. (19.2.47) is s2X ¼ s2X =(1 f2 ) ¼ 1=ð1 0:64Þ ¼ 2:778 with mean mX ¼ 0. This process Xn simulated on a computer with 10,000 points of the Gaussian noise sequence generated previously is shown in Fig. 20.6.2a for only 200 points for clarity. The variance of the simulated process is 2.905, which is very close to the true value of 2.778. The histogram of the data of 10,000 points is shown in Fig. 20.6.2b. On the same histogram plot is shown the true pdf of Xn and the pdf of true white-noise sequence Vn.
B 20.7
WIENER PROCESS (BROWNIAN MOTION)
The random motion of a particle in a fluid subject to collisions and influence of other particles is called Brownian motion. One of the mathematical models of this motion is the Wiener process. If the following conditions are satisfied, W(t) is a Wiener process with parameter s2: 1. W(0) ¼ 0. 2. W(t) is a Gaussian process with sample continuous functions. 3. E[W(t)] ¼ 0. 4. The autocovariance function CW(t, s) ¼ RW(t, s) ¼ E[W(t), W(s)] ¼ s2 min(t, s). 5. The variance of W(t) from (4) is s2W (t) ¼ s2 t. The pdf of the Wiener process is given by 1 2 2 fW (w, t) ¼ pffiffiffiffiffiffiffiffiffiffi e(1=2)(w =s t) 2pst
(20:7:1)
The covariance matrix of the random variable vector WT ¼ ½W(t1 ), W(t2 ), . . . , W(tn )T for times 0 , t1 , t2 , . . . , tn can be given from condition 4 as 2 3 t1 t1 t1 t1 6 t1 t2 t2 t2 7 6 7 T 2 6 t1 t2 t3 t3 7 (20:7:2) CW ¼ E ½WW ¼ s 6 7 6. . . .. 7 4 .. .. .. 5 . t1 t2 t3 tn
524
Classification of Random Processes
and this matrix is positive definite. Since W(t) is Gaussian, the finite-dimensional density function using Eq. (20.7.2) is given by fW (w1 , t1 ; w2 , t2 ; . . . ; wn , tn ) ¼
T 1 1 e(1=2)W CW W 1=2 (2p) jCW j
n=2
(20:7:3)
The determinant jCW j ¼ t1 (t2 t1 ) (tn1 tn ) and Eq. (20.7.3) can be rewritten as
n Y 1 1 (wk wk1 )2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp fW (w1 , t1 ; w2 , t2 ; . . . ; wn , tn ) ¼ 2 2 s2 (tk tk1 ) k¼1 2p(tk tk1 )s (20:7:4) with t0 ¼ 0 ¼ w0. The density function of Eq. (20.7.4) shows that the sequence {Wt1 , (Wt2 Wt1 ), . . . , (Wtn Wtn1 )} is a collection of independent random variables for increasing f0 , t1 , t2 , . . . , tng and the density depends only on the time difference, showing that the Wiener process is a process of stationary independent increments. Hence, it is nonstationary and is a continuous-state continuous-time (CSCT) process. Derivation of Wiener Process
We will derive the Wiener process as the limiting form of the elementary randomwalk process discussed in Section 20.4, which will be restated in a form where limits can be taken. A particle starts from origin at t ¼ 0. In a small interval Dt it undergoes a small step +Dw with equal probabilities, thus constituting a symmetric random walk. The number n of Bernoulli trials in the time interval (0, t) is equal to the greatest integer less than or equal to t/Dt, or jtk n¼ Dt where bxc represents the greatest integer less than or equal to x, called the “floor function.” Similar to the definition in Eq. (20.4.11), the Bernoulli random variable Zi is defined by þ1 if the ith step of Dw is upward Zi ¼ 1 if the ith step of Dw is downward with probabilities 1 2 1 P(Zi ¼ 1) ¼ 2
P(Zi ¼ þ1) ¼
(20:7:5)
The mean and variance of Zi are given by E ½Zi ¼ 0 and var ½Zi ¼ 1. If the position of the particle at time nDt is W(nDt), then W(nDt) ¼ Dw
n X i¼1
Zi u(t iDt)
(20:7:6)
20.7
Wiener Process (Brownian Motion)
525
The mean and variance of the process W(nDt) are E ½W(nDt) ¼ 0 and
var ½W(nDt) ¼ E ½W 2 (nDt) ¼ Dw2 n ¼ Dw2
jtk Dt
(20:7:7)
We now let Dw and Dt tend to zero in such a way that Eq. (20.7.7) is nontrivial. Hence we have to set pffiffiffiffiffi Dw ¼ s Dt (20:7:8) where s is a positive constant so that as Dt ! 1, W(t) ¼ lim s Dt!0
1 X
pffiffiffiffiffi Zi u(t iDt) Dt
i¼1
and lim Dw2
Dt!0
jtk ! s2 t Dt
(20:7:9)
W(t) is the desired Wiener process, and a sample function is shown in Fig. 20.7.1. The statistics of the Wiener process are as follows: Mean. Since E[Zi] ¼ 0, we have E ½W(t) ¼ mW (t) ¼ 0
(20:7:10)
Variance. From Eq. (20.7.7), we obtain var½W(t) ¼ E ½W 2 (t) ¼ s2W (t) ¼ s2 t
(20:7:11)
Autocorrelation. Since E[W(t)] ¼ 0, RW (t1, t2) ¼ CW (t1, t2). For t1 , t2: RW (t1 , t2 ) ¼ CW (t1 , t2 ) ¼ E½W(t1 )W(t2 ) ¼ E{½W(t2 ) W(t1 ) þ W(t1 )W(t1 )}: From linearity of expectations: RW (t1 , t2 ) ¼ E{½W(t2 ) W(t1 )W(t1 )} þ E ½W 2 (t1 ). From independent increments: RW (t1 , t2 ) ¼ E ½W 2 (t1 ) ¼ s2 t1 . Similarly, for t2 , t1, we have RW (t1 , t2 ) ¼ E ½W 2 (t2 ) ¼ s2 t2 .
FIGURE 20.7.1
526
Classification of Random Processes
Hence RW (t1 , t2 ) ¼ CW (t1 , t2 ) ¼ s2 min(t1 , t2 )
(20:7:12)
The Wiener process has the following properties: 1. From the properties of the random walk from which W(t) was constructed, we conclude that for (0 , t , s) the increments W(t) 2 W(0) and W(s) 2 W(t) are independent. 2. From Eq. (20.7.1), for t2 , t1 the density function of the increment W(t2) 2 W(t1) is 2 2 1 2 fW (w1 , t1 ; w2 , t2 ) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi eð1=2Þ½(w2 w1 ) =s (t2 t1 ) 2 2p(t2 t1 )s
(20:7:13)
and since it depends only on the time difference (t2 2 t1), W(t) is a stationary independent increment process. 3. Since W(t) is the limit of a symmetric random walk, it can be conjectured that it is a continuous process for all t . 0. Indeed it can be shown [35,37] that it is continuous everywhere with probability 1. 4. It can be shown [35,37] that W(t) is of unbounded variation, and as a result it is differentiable nowhere in the conventional sense. Property 4 has far-reaching consequences. If we have any function g[W(t)] of another nonrandom function W(t), then g[W(t þ dt)] can be expanded in a Taylor series as g½W(t þ dt) ¼ g½W(t) þ
dg½W(t) 1 d2 g½W(t) dW(t) þ dW 2 (t) þ dw 2 dw2
(20:7:14)
Neglecting higher-order terms Eq. (20.7.14), we can write dg[W(t)] as g½W(t þ dt) g½W(t) ¼ dg½W(t) ¼
dg½W(t) dW(t) dw
(20:7:15)
However, if W(t) is a Wiener process, the term dW 2(t) cannot be neglected because from Eq. (20.7.8) it is of the order of dt. Hence the differential dg[W(t)] can be written only as g½W(t þ dt) g½W(t)
dg½W(t) 1 d2 g½W(t) dW(t) þ dW 2 (t) dw 2 dw2 dg½W(t) 1 d 2 g½W(t) dW(t) þ s2 dt dw 2 dw2
(20:7:16)
neglecting only third and higher-order terms. Thus the differential dg[W(t)] does not obey the ordinary laws of calculus! It is also interesting to note that if W(t) is a deterministic process, then s2 is equal to zero and Eq. (20.7.16) reverts to the usual calculus. For more details on this interesting aspect, see Refs. 35 and 37. Formal Derivative of Wiener Process
We have stated in properties 3 and 4 that the Wiener process is continuous everywhere but differentiable nowhere in the conventional sense. However, we will formally
20.8
Markov Process
527
differentiate it and obtain its autocorrelation function. If Z(t) ¼ W(t) is the formal derivative of the Wiener process, it can be shown from Eq. (19.2.31b) that the autocorrelation function of Z(t) ¼ W(t) is given by RZ (t, s) ¼ RW (t, s) ¼ E ½W(t)W(s) ¼
@2 RW (t, s) @t @s
(20:7:17)
and if s ¼ t þ t, we obtain Eq. (19.2.31b). We substitute RW (t, s) ¼ s2 min(t, s) from Eq. (20.7.12) in Eq. (20.7.17) and obtain RW (t, s) ¼ RZ (t, s) ¼ E ½Z(t)Z(s) ¼
@2 2 s min(t, s) ¼ s2 d(t s) @t @s
(20:7:18)
From Eq. (20.7.18) we conclude that since the autocorrelation of the derivative of the Wiener process yields a Dirac delta function, it exists only in the generalized sense. However, the autocorrelation of Z(t) ¼ W(t) is a function of the time difference (t 2 s). It is also a zero mean Gaussian process. Hence the formal derivative Z(t) of a Wiener process is a zero mean stationary Gaussian process with power spectral density s2 that is constant in the entire frequency range. This is the Gaussian white noise that has been used extensively to model communication and time-series problems. The similarity between the Gaussian and Poisson white noises [Eq. (20.2.39)] is to be noted.
B 20.8
MARKOV PROCESS
It was stated in Section 19.1 that a random process X(t) is completely specified if the nth-order joint density given by Eq. (19.1.9) for an ordered set of time functions ft1 , t2 , t3 , . . . , tng fX (xn , . . . , x1 ; tn , . . . , t1 ) ¼
@n FX (xn , . . . , x1 ; tn , . . . , t1 ) @xn @x1
(20:8:1)
is specified for all positive integers n. The joint density given by Eq. (20.8.1) can be decomposed into n first-order conditional densities for the same set of ordered time functions as shown below: fX (x1 ; t1 ) fX (x2 ; t2 j x1 ; t1 ) fX (x3 ; t3 j x2 , x1 ; t2 , t1 ) fX (xn1 ; tn1 j xn2 , . . . , x2 , x1 ; tn2 , . . . , t2 , t1 )
(20:8:2)
fX (xn ; tn j xn1 , . . . , x2 , x1 ; tn1 , . . . , t2 , t1 ) By repeated application of the definition of conditional probability, the n-dimensional joint density of Eq. (20.8.1) can be obtained from Eq. (20.8.2). Thus, for the random process to be completely determined, we should have n ! 1 of these first-order conditional densities. However, the specifications for a class of random processes can be considerably simplified if we can express their conditional densities as fX (xn ; tn jxn1 , . . . , x2 , x1 ; tn1 , . . . , t2 , t1 ) ¼ fX (xn ; tn jxn1 ; tn1 )
(20:8:3)
What Eq. (20.8.3) conveys is that the probabilistic behavior of the present given all past history is dependent only on the immediate past. The property exhibited by Eq. (20.8.3) is called the Markov property, and the class of processes having this property is known as
528
Classification of Random Processes
Markov processes. Using the Markov property the n-dimensional density can be given as follows: fX (xn , . . . , x1 ; tn , . . . , t1 ) ¼ f (xn ; tn j xn1 ; tn1 )fX (xn1 , . . . , x1 ; tn1 , . . . , t1 )
(20:8:4)
By applying the Markov property repeatedly to lower-order density functions, we can write the n-dimensional density as fX (xn , . . . , x1 ; tn , . . . , t1 ) ¼ fX (x1 ; t1 )
n Y
f (xi ; ti j xi1 ; ti1 )
(20:8:5)
i¼2
Hence all finite-dimensional densities of a Markov process are completely determined by the initial marginal density fX (x1 ; t1 ) and the set of first-order conditional densities, {f (xi ; ti j xi1 ; ti1 ), i ¼ 2, 3, . . .}. The conditional probability density f (xi ; ti j xi1 ; ti1 ) is called the transition probability density. For a discrete-time Markov process, the equation corresponding to Eq. (20.8.3) can be written as follows: P{X(tn ) xn j X(tn1 ) xn1 , . . . , X(t1 ) x1 } ¼ P{X(tn ) xn j X(tn1 ) xn1 } (20:8:6) What we have shown is that given a Markov process, its n-dimensional density can be expressed by the initial marginal density and a set of first-order conditional densities. However, to construct a Markov process from the given first-order conditional densities, the following consistency conditions have to be satisfied: ð1 1. fX (x2 ; t2 ) ¼ fX (x2 ; t2 j x1 ; t1 ) fX (x1 ; t1 ) dx2 , t1 , t2 (20:8:7) 1
This equation is the usual equation that relates a joint density to its marginal density. ð1 (x ; t j x ; t ) ¼ fX (x3 ; t3 j x2 ; t2 ) fX (x2 ; t2 j x1 ; t1 ) dx2 , t1 , t2 , t3 (20:8:8) f 2. X 3 3 1 1 1
This equation is called the Chapman– Kolmogorov equation, which all Markov processes must satisfy. We will show that Markov processes satisfy this equation. The joint pdf fX (x3 , x1 ; t3 , t1 ) with t1 , t2 , t3 can be written as ð1 fX (x3 , x1 ; t3 , t1 ) ¼ fX (x3 , x2 , x1 ; t3 , t2 , t1 ) dx2 (20:8:9) 1
Using the property of conditional expectation twice, we can rewrite Eq. (20.8.9) as, ð1 fX (x3 , x1 ; t3 , t1 Þ ¼ fX (x3 ; t3 j x2 , x1 ; t2 , t1 )fX (x2 , x1 ; t2 , t1 ) dx2 1
ð1
fX (x3 ; t3 j x2 , x1 ; t2 , t1 )fX (x2 ; t2 j x1 ; t1 )fX (x1 ; t1 ) dx2
¼ 1
(20:8:10) Using the Markov property fX (x3 ; t3 j x2 , x1 ; t2 , t1 ) ¼ fX (x3 ; t3 j x2 ; t2 ) in Eq. (20.8.10), we obtain ð1 fX (x3 , x1 ; t3 , t1 ) ¼ fX (x1 ; t1 ) fX (x3 ; t3 j x2 ; t2 )fX (x2 ; t2 j x1 ; t1 ) dx2 (20:8:11) 1
20.8
Markov Process
529
Dividing Eq. (20.8.11) throughout by fX (x1 ; t1 ), for t1 , t2 , t3 , we have fX (x3 , x1 ; t3 , t1 ) ¼ fX (x3 , t3 j x1 ; t1 ) fX (x1 ; t1 ) ð1 ¼ fX (x3 ; t3 j x2 ; t2 ) fX (x2 ; t2 j x1 ; t1 ) dx2
(20:8:8)
1
which is the desired Chapman–Kolmogorov (C–K) equation. This derivation also holds good for discrete-time processes, and the result is X P{X(t3 ) x3 j X(t1 ) x1 } ¼ P{X(t3 ) x3 j X(t2 ) x2 } x2
P{X(t2 ) x2 j X(t1 ) x1}
(20:8:12)
The state fx2, t2) represents the intermediate state, and operation of the C – K equation is shown in Fig. 20.8.1. The C – K equation is a sufficient condition for a Markov process in the sense that nonMarkovian processes may also satisfy this equation. We can also define a Markov process in terms of conditional expectations. If g() is any function with maxx jg(x)j , 1, then X(t) is a Markov process for an ordered sequence ft1 , t2 , . . . , tng if and only if E{g½X(tn ) j X(tn1 ), X(tn2 ), . . . , X(t1 )} ¼ E{g½X(tn ) j X(tn1 )}
(20:8:13)
If the conditional probability P{X(tn ) xn j X(tn1 ) xn1 } of a Markov process is dependent only on the time difference (tn tn1 ), it is called a homogeneous process. In other words, if (t2 t1 ) ¼ (tn tn1 ), then homogeneity implies P{X(tn ) xn j X(tn1 ) xn1 } ¼ P{X(t2 ) x2 j X(t1 ) x1 } Generally, a homogenous Markov process may not be stationary since PfX(t1) x1g may depend on t1. We have shown earlier that a stationary independent increment process is always nonstationary in light of Eqs. (20.4.2a) and (20.4.2b). We will now show that this process is always a Markov process. Let X(t) be a stationary independent increment process with
FIGURE 20.8.1
530
Classification of Random Processes
t0 ¼ 0 and X(t0) ¼ x0 ¼ 0. For all ti , tj, the events {X(ti ) ¼ xi , X(tj ) ¼ xj
and
{X(ti t0 ) ¼ xi x0 , X(tj ti ) ¼ xj xi }
are equal, and hence their joint densities are also equal. As a consequence fX (xj ; tj , xi ; ti ) ¼ fX (xj xi ; tj ti , xi x0 ; ti t0 ),
i,j
(20:8:14)
With x0 ¼ t0 ¼ 0 and using the independent increment property, Eq. (20.8.14) can be rewritten after dropping the time difference (tj 2 ti), due to stationarity: fX (xj ; tj , xi ; ti ) ¼ fX (xj xi ) fX (xi ; ti ),
i,j
(20:8:15)
Analogous to Eq. (20.8.15), for the sequence ftn , tn21 , . . . , t1 , t0 ¼ 0g, we can write fX (xn , . . . ; x1 , x0 ; tn , . . . , t1 , t0 ) ¼ fX (xn xn1 , . . . , x2 x1 , x1 x0 ; tn tn1 , . . . , t2 t1 , t1 t0 ) ¼ fX (xn xn1 ) fX (x2 x1 ) fX (x1 ; t1 )
(20:8:16)
From Eq. (20.8.15) we can substitute fX (xk xk1 ) ¼
fX (xk , xk1 ; tk , tk1 ) , fX (xk1 ; tk1 )
k ¼ n, n 1, . . . , 2
in Eq. (20.8.16) and write fX (xn , . . . x1 ; tn , . . . , t1 ) ¼
fX (xn , xn1 ; tn , tn1 ) fX (x2 , x1 ; t2 , t1 ) fX (xn1 ; tn1 ) fX (x2 ; t2 )
(20:8:17)
Equation (20.8.17) expresses the nth-order joint density of a stationary independent increment process X(t) in terms of the second- and first-order densities. From the definition of conditional density, we can write fX (xn ; tn j xn1 , . . . , x1 ; tn1 , . . . , t1 ) ¼
fX (xn , xn1 x1 ; tn , tn1 , . . . , t1 ) fX (xn1 x1 ; tn1 , . . . , t1 )
(20:8:18)
Applying Eq. (20.8.17) to both numerator and denominator of Eq. (20.8.17), we obtain fX (xn ; tn j xn1 , . . . , x1 ; tn1 , . . . , t1 ) fX (xn , xn1 ; tn , tn1 ) fX (x2 , x1 ; t2 , t1 ) fX (xn1 ; tn1 ) fX (x2 ; t2 ) ¼ fX (xn1 ; tn , tn1 ) fX (x2 , x1 ; t2 , t1 ) fX (xn2 ; tn2 ) fX (x2 ; t2 ) ¼
(20:8:19)
fX (xn , xn1 ; tn , tn1 ) ¼ fX (xn ; tn j xn1 ; tn1 ) fX (xn1 ; tn1 )
which is the defining equation of a Markov process. However, the converse is not true, or, not every Markov process is a stationary independent increment process. Summary of Properties of Markov Processes 1.
fX (xn ; tn j xn1 ; tn1 , . . . , x1 ; t1 ) ¼ fX (xn ; tn j xn1 ; tn1 )
(20:8:20)
20.8
Markov Process
531
Applying the chain rule for conditional expectations from Eq. (20.8.5), we can write fX (xn ; tn j xn1 ; tn1 , . . . , x1 ; t1 ) ¼
fX (xn ; tn , xn1 ; tn1 , . . . , x1 ; t1 ) fX (xn1 ; tn1 , . . . , x1 ; t1 )
¼ fX (xn ; tn j xn1 ; tn1 )
(20:8:21)
2. This rule can be generalized to any sequence of times ftnk . tnk21 . . . . . tn1g: fX (xnk ; tnk j xnk1 ; tnk1 , . . . , xn1 ; tn1 ) ¼ fX (xnk ; tnk j xnk1 ; tnk1 ) 3. A time-reversed Markov process is also a Markov process: fX (xn ; tn j xnþ1 ; tnþ1 , . . . , xnþk ; tnþk ) ¼ fX (xn ; tn j xnþ1 ; tnþ1 )
(20:8:22)
4. Given the present information, the past and the future are conditionally independent. Or, for k , m , n, we obtain fX (xn ; tn , xk ; tk j xm ; tm ) ¼ fX (xn ; tn j xm ; tm ) f (xk ; tk j xm ; tm )
(20:8:23)
We will show that this result is true: fX (xn ; tn , xk ; tk j xm ; tm ) ¼ ¼
fX (xn ; tn , xm ; tm , xk ; tk ) fX (xm ; tm ) fX (xn ; tn j xm ; tm ) fX (xm ; tm j xk ; tk ) fX (xk ; tk ) fX (xm ; tm )
(chain rule)
Substituting for fX (xm ; tm j xk ; tk ) ¼
fX (xk ; tk j xm ; tm ) fX (xm ; tm ) fX (xk ; tk )
in the preceding equation, we obtain Eq. (20.8.23). 5. Taking conditional expectations in property 3, we have E ½X(tn )X(tk ) j X(tm ) ¼ xm ¼ E ½X(tn ) j X(tm ) ¼ xm ¼ E ½X(tk ) j X(tm ) ¼ xm
(20:8:24)
6. The future, conditioned on the past history upto the present, is the future conditioned on the present: E{X(tnþ1 ) j X(tn ), X(tn1 ), . . . , X(t1 )} ¼ E{X(tnþ1 ) j X(tn )} This equation is a restatement of Eq. (20.8.20).
(20:8:25)
532
Classification of Random Processes
7. If t1 , t2 , t3, then E ½X(t3 ) j X(t1 ) ¼ E{E ½X(t3 ) j X(t2 )jX(t1 )}
(20:8:26)
This equation is a restatement of the C –K property in terms of conditional expectations.
Example 20.8.1 (Poisson Process) The Poisson process N(t) is an independent increment process from Eq. (20.2.10.). It is also a stationary independent increment process, because the probability of the increment, N(t2) 2 N(t1), given by P{N(t2 ) N(t1 ) ¼ k2 k1 } ¼
½l(t2 t1 )k2 k1 l(t2 t1 ) e (k2 k1 )!
is dependent only on time difference (t2 2 t1). From the result of Eq. (20.8.19), we can conclude that the Poisson process is a homogeneous Markov process. However, it will be instructive to show the Markov property from the definition of Eq. (20.8.20). The second-order probability function is given by P{N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } ¼ P{N(t2 ) ¼ k2 jN(t1 ) ¼ k1 }P{N(t1 ) ¼ k1 } The conditional probability N(t2 ) ¼ k2 given that N(t1 ) ¼ k1 is the probability that there are k2 2 k1 events occurring in the time interval t2 2 t1: P{N(t2 ) ¼ k2 jN(t1 ) ¼ k1 } ¼ P{N(t2 ) N(t1 ) ¼ k2 k1 } ¼
½l(t2 t1 )k2 k1 l(t2 t1 ) e , (k2 k1 )!
k2 k1
Hence the second-order probability function P{N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } is given by P{N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } ¼
½lt1 )k1 ½l(t2 t1 )k2 k1 lt2 e , k1 !(k2 k1 )!
k2 k1
Proceeding in a similar manner, we can obtain the third-order probability function as P{N(t3 ) ¼ k3 , N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } ¼
½lt1 )k1 ½l(t2 t1 )k2 k1 ½l(t3 t2 )k3 k2 lt3 e , k1 !(k2 k1 )!(k3 k2 )!
k3 k2 k1
The second-order probability function P{N(t3 ) ¼ k3 , N(t2 ) ¼ k2 } can be evaluated as P{N(t3 ) ¼ k3 , N(t2 ) ¼ k2 } ¼ P{N(t3 ) N(t2 ) ¼ k3 k2 }P{N(t2 ) ¼ k2 } ¼
½lt2 )k2 ½l(t3 t2 )k3 k2 lt3 e , k2 !(k3 k2 )!
k3 k2
20.8
Markov Process
533
Combining all these equations, we have
P{N(t3 ) ¼ k3 j N(t2 ) ¼ k2 } ¼
P{N(t3 ) ¼ k3 , N(t2 ) ¼ k2 } P{N(t2 ) ¼ k2 }
½lt2 k2 ½l(t3 t2 )k3 k2 lt3 e ½l(t3 t2 )k3 k2 l(t3 t2 ) k2 !(k3 k2 )! e ¼ ¼ k2 (k3 k2 )! ½lt2 lt2 e k2 ! P{N(t3 ) ¼ k3 j N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } ¼
P{N(t3 ) ¼ k3 , N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } P{N(t2 ) ¼ k2 , N(t1 ) ¼ k1 }
½lt1 )k1 ½l(t2 t1 )k2 k1 ½l(t3 t2 )k3 k2 lt3 e k1 !(k2 k1 )!(k3 k2 )! ¼ ½lt1 )k1 ½l(t2 t1 )k2 k1 lt2 e k1 !(k2 k1 )! ¼
½l(t3 t2 )k3 k2 l(t3 t2 ) e (k3 k2 )!
Hence P{N(t3 ) ¼ k3 j N(t2 ) ¼ k2 , N(t1 ) ¼ k1 } ¼ P{N(t3 ) ¼ k3 j N(t2 ) ¼ k2 }, showing that N(t) is indeed a Markov process. Since the conditional density
P{N(t2 ) ¼ k2 jN(t1 ) ¼ k1 } ¼
½l(t2 t1 )k2 k1 l(t2 t1 ) e , (k2 k1 )!
k2 k1
is a function of the time difference (t2 2 t1), we conclude further that the Poisson process is also homogeneous Markov process.
Example 20.8.2 (Wiener Process) process as given by Eq. (20.7.4) is
The n-dimensional density function of a Wiener
( ) 1 1 (wk wk1 )2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp fW (w1 , w2 , . . . , wn ; t1 , t2 , . . . , tn ) ¼ 2 2 s2 (tk tk1 ) k¼1 2p(tk tk1 )s n Y
with t0 ¼ 0 ¼ w0 and the sequence
{Wt1 , (Wt2 Wt1 ), . . . , (Wtn Wtn1 )}
534
Classification of Random Processes
is a collection of independent random variables for increasing f0 , t1 , t2 , . . . , tng. We have to show that fW (wn j wn1 , . . . , w1 ) ¼ fW (wn j wn1 ) or fW (wn , wn1 , . . . , w1 ) fW (wn , wn1 ) ¼ fW (wn1 , . . . , w1 ) fW (wn1 )
1 1 (wk wk1 )2 ffi exp k¼1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 s2 (tk tk1 ) fW (wn , wn1 , . . . , w1 ) 2p(tk tk1 )s2 ¼
fW (wn1 , . . . , w1 ) Qn1 1 1 (wk wk1 )2 p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi exp k¼1 2 s2 (tk tk1 ) 2p(tk tk1 )s2
1 1 (wn wn1 )2 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp 2 s2 (tn tn1 ) 2p(tn tn1 )s2 Qn
1 1 (wn wn1 )2 exp 2p(tn tn1 )(tn1 tn2 )s4 2 s2 (tn tn1 )
1 (wn1 wn2 )2 exp fW (wn , wn1 ) 2 s2 (tn1 tn2 ) ¼
fW (wn1 ) 1 1 (wn1 wn2 )2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp 2 s2 (tn1 tn2 ) 2p(tn1 tn2 )s2
1 1 (wn wn1 )2 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp 2 s2 (tn tn1 ) 2p(tn tn1 )s2 which shows that W(t) is a homogeneous Markov process with stationary independent increments. Example 20.8.3 (AR Process) given by
In Example 20.6.1 an autoregressive(1) process was
Xn ¼ fXn1 þ Vn ,
n ¼ 1, . . . , X0 ¼ V0
where fVn, n ¼ 0, 1. . .g is a Gaussian process with zero mean and variance s2V. The conditional density fX (xn j Xn21 ¼ xn21) is also Gaussian-distributed with mean fxn21 and variance s2V. This is a Markov process since the conditional density function depends only on the Xn21 and not any of the previous values. Thus fX (xn j Xn1 ¼ xn1 , . . . , X0 ¼ x0 ) ¼ fX (xn j Xn1 ¼ xn1 ) Example 20.8.4 (Polya Urn Model) Initially at n ¼ 0, an urn contains b blue balls and r red balls. In each trial, a ball is drawn at random and its color recorded. It is then replaced with c balls of the same color. Let Xn be the random variable representing the color of the ball at the nth draw. We will also assume that Xk ¼ 1 if the ball selected at the kth trial is
20.8
Markov Process
535
blue and Xk ¼ 0 if it is red. The probability of X1 ¼ 1 is P{X1 ¼ 1} ¼
b bþr
The probability of X2 ¼ 1 conditioned on X1 ¼ 1 is P{X2 ¼ 1 j X1 ¼ 1} ¼
bþc bþcþr
Hence, the joint probability PfX2 ¼ 1, X1 ¼ 1g P{X2 ¼ 1, X1 ¼ 1} ¼
b bþc bþr bþcþr
The probability of X3 ¼ 1 conditioned on X2 ¼ 1 and X1 ¼ 1 is b þ 2c b þ 2c þ r
P{X3 ¼ 1 j X2 ¼ 1, X1 ¼ 1} ¼ and the joint probability PfX3 ¼ 1, X2 ¼ 1, X1 ¼ 1g is P{X3 ¼ 1, X2 ¼ 1, X1 ¼ 1} ¼
b þ 2c bþc b b þ 2c þ r b þ c þ r b þ r
The realization fX6 ¼ 0, X5 ¼ 1, X4 ¼ 0, X3 ¼ 0, X2 ¼ 1, X1 ¼ 1g can be interpreted as the event of drawing 3 blue balls in 6 trials, and the probability of this event is p(3, 6). Thus P{X6 ¼ 0, X5 ¼ 1, X4 ¼ 0, X3 ¼ 0, X2 ¼ 1, X1 ¼ 1} ¼ p(3, 6) ¼
r þ 2c b þ 2c rþc r bþc b b þ 5c þ r b þ 4c þ r b þ 3c þ r b þ 2c þ r b þ c þ r b þ r
Extending these results, the probability of a sequence of k blue balls and (n 2 k) red balls defined by p(k, n) can be determined as follows: p(k, n) ¼ P{Xn ¼ xn , Xn1 ¼ xn1 , . . . , X1 ¼ x1 } ¼
b(b þ c)(b þ 2c) ½b þ (k 1)cr(r þ c)(r þ 2c) ½r þ (n k 1)c (b þ r)(b þ c þ r)(b þ 2c þ r) ½b þ r þ (n 1)c
This random sequence is an example of a discrete-state discrete-time (DSDT) random process. It is nonstationary because the probability is dependent on time, n. From the discussion above, we have P{X3 ¼ 1jX2 ¼ 1} ¼
bþc bþcþr
but P{X3 ¼ 1jX2 ¼ 1, X1 ¼ 1} ¼
b þ 2c b þ 2c þ r
Since P{X3 ¼ 1jX2 ¼ 1, X1 ¼ 1} = P{X3 ¼ 1jX2 ¼ 1}, the sequence fXng is not a Markov process.
536
Classification of Random Processes
B 20.9
MARKOV CHAINS
When the state of a Markov process is discrete, we call it a Markov chain (MC). If the time is also discrete, it is called a discrete-time Markov chain. The binomial process (Example 20.4.2) and the random-walk process (Example 20.4.3) are illustrations of a discrete Markov chain. If the time is continuous, it is called a continuous-time Markov chain. The Poisson process discussed in detail in Section 20.2 is an example of a continuoustime Markov chain. These processes have been extensively studied, and many physical phenomena are modeled by them. We will now discuss discrete Markov chains. For continuous Markov chains, see Refs. 13, 30, and 54.
Discrete Markov Chain
A random process fXn, n ¼ 0, 1, . . .g with discrete state space S is a Markov chain if the following probability relation holds for each f j, in, . . . , i0g [ S: P{Xnþm ¼ j j Xn ¼ in , . . . , X0 ¼ i0 } ¼ P{Xnþm ¼ j j Xn ¼ in } ¼ p(m) in j (n)
(20:9:1)
The quantity P{Xnþm ¼ j j Xn ¼ in } ¼ p(m) in j (n) is the conditional probability of transition from state in at time n to state j at time n þ m. It is referred to as the m-step transition probability, which in general will be a function of time n. However, if the transition from state i to state j is dependent only on the time difference m ¼ n þ m 2 n and not on the current time n, then the transition probability is stationary and the Markov chain is homogeneous, or P{Xnþm ¼ j j Xn ¼ i} ¼ P{Xm ¼ j j X0 ¼ i} ¼ p(m) ij The one-step transition probability of a homogeneous Markov chain defined as pij ¼ P{Xnþ1 ¼ j j Xn ¼ i} ¼ P{X1 ¼ j j X0 ¼ i}
(20:9:2) p(1) ij
¼ pij can be (20:9:3)
If the number of states N is finite, the Markov chain is finite; otherwise it is infinite. The one-step probabilities fpijg can be expressed as either a transition matrix P or a state transition flow diagram. To make these ideas clear, we will consider the modified Bernoulli process given in Eq. (20.4.11), where the random variable Xn at time n is in state 1 with probability p(n) 0 or in state 21 with probability p(n) 1 as given below: P(Xn ¼ 1) ¼ p(n) 0
(20:9:4)
P(Xn ¼ 1) ¼ p(n) 1
Further, if the particle is in state 1 at time n, then the probability of transition to 21 is a at time (n þ 1), and if it is in state 21 at n, then the probability of transition to 1 is b at the time (n þ 1). We are considering a two-state homogeneous Markov chain. The transition matrix P is shown in Eq. (20.9.5): 1 P¼
1
1 1a a 1 b 1b
The state transition flow diagram is shown in Fig. 20.9.1.
(20:9:5)
20.9
Markov Chains
537
FIGURE 20.9.1
If there are Nþ1 states and the transition probability from state i to state j is pij, then the one-step transition matrix P is given by 2
0 p00
8 0 > > > 6 p10 > > 16 > 6 . > > 6 < 6 .. 6 P¼ 6p > i > 6 i0 > > 6 . > > 6 . > > : 4 . N pN0
1 p01
j p0j
p11 .. . pi1 .. .
p1j .. . pij .. .
pN1
pNj
N 3 p0N p1N 7 7 .. 7 7 . 7 7 piN 7 7 .. 7 7 . 5
(20:9:6)
pNN
The matrix P is called a stochastic matrix and has the property that the row sum equals 1: N X pij ¼ 1, i ¼ 0, 1, . . . , N (20:9:7) j¼0
The m-step transition probability for all i, j defined in Eq. (20.9.2) can also be given in matrix form as follows:
P
(m)
0 8 2 (m) 0 p00 > > > > > 16 > 6 p(m) > 10 > > 6 > 6 . > < 6 .. 6 ¼ 6 (m) > 6 pi0 > > 6 i > > 6 . > > 6 . > > 4 . > > : N p(m) N0
1 p(m) 01 p(m) 11
j
p(m) 0j p(m) 1j
.. .
.. .
p(m) i1
p(m) ij
.. .
.. . p(m) N1
p(m) Nj
N
p(m) 0N p(m) 1N
3
7 7 7 .. 7 . 7 7 (m) 7 piN 7 7 .. 7 7 . 5 p(m) NN
(20:9:8)
Let us consider the two-step transition from state i at time 0 through an intermediate state k at time m 2 1 to state j at time m. From the property of conditional expectations we can write X P{Xm ¼ j, X0 ¼ i} ¼ P{Xm ¼ j, Xm1 ¼ k, X0 ¼ i} k
¼
X
P{Xm ¼ j j Xm1 ¼ k, X0 ¼ i}
k
P{Xm1 ¼ k j X0 ¼ i}Pf X0 ¼ i}
(20:9:9)
Dividing Eq. (20.9.9) throughout by PfX0 ¼ ig and using the Markov property, P{Xm ¼ j j Xm1 ¼ k, X0 ¼ i} ¼ P{Xm ¼ j j Xm1 ¼ k}, we obtain the following equation
538
Classification of Random Processes
for all i, j: P{Xm ¼ j j X0 ¼ i} ¼ p(m) ij ¼
X
P{Xm ¼ j j Xm1 ¼ k}P{Xm1 ¼ k j X0 ¼ i}
k
¼
X
pkj p(m1) ik
(20:9:10)
k
This equation is a restatement of the Chapman –Kolmogorov equation [Eq. (20.8.12)]. Similar equations can be written for p(m1) in terms of pkj and p(m2) . Thus, in matrix ij ik form P(m) ¼ PP(m1)
or P(m) ¼ Pm
Eq. (20.9.11) is an important result that shows the m-step transition matrix P mth power of the one-step transition matrix P.
(20:9:11) (m)
as the
State Occupancy Probabilities (n) (n) Let the row vector p(n) ¼ ½p(n) represent the state occupancy 0 , p0 , . . . , pN (0) (0) (0) probabilities at time n and the row vector p ¼ ½p(0) 0 , p1 , . . . , pN represent the initial probability of the states at time 0. Since p is the state transition probability matrix, we can obtain the state occupancy probabilities p (n) of any state at time n from a knowledge of the probability of the state at time n21 from the following compact matrix relation:
p(n) ¼ p(n1) P
(20:9:12)
This equation can be solved recursively, and the probabilities of the states at time n can be related to the initial probabilities of the states at time 0 as given below: p(n) ¼ p(n1) P ¼ p(n2) P2 ¼ ¼ p(0) Pn
(20:9:13)
The next logical question that may arise is whether after a sufficiently long time a statistical equilibrium can be established in the sense that the occupancy probabilities of the states are independent of the initial probabilities. To answer that question, we set limn!0 p(n) ¼ p in Eq. (20.9.12) and obtain lim p(n) ¼ lim p(n1) P
n!0
n!0
or
p ¼ pP
or p(I P) ¼ 0
(20:9:14)
The matrix equation p(I P) is a homogeneous system of linear equations and has a solution only if the determinant of (I 2 P) is equal to 0. This is very similar to the eigenvector [Eq. (16.2.2)]. Solution of Eq. (20.9.14) will give the row vector p only P upto a mulitiplicative constant, and it can be made unique by the normalization equation Ni¼0 pi ¼ 1. By utilizing the techniques of Chapter 16, we can obtain the solution for a finite Markov chain by diagonalizing the matrix P. The eigenvalues of the matrix P are obtained by forming the characteristic equation jlI 2 Pj ¼ lN þ a1lN21 þ . . . þ aN ¼ 0 and solving for the N eigenvalues. If we substitute l ¼ 1 in the eigenequation lI 2 P ¼ 0, we obtain I 2 P ¼ 0. Since the row sum of the stochastic matrix is 1 and all the terms are positive, we conclude that l ¼ 1 is always an eigenvalue of the stochastic matrix P and all the other eigenvalues will be less than 1. We can diagonalize the matrix P assuming that the eigenvalues are distinct and write M1 PM ¼ L
or
P ¼ MLM1
(20:9:15)
20.9
Markov Chains
539
where M is the modal matrix of eigenvectors and L is a diagonal matrix of distinct eigenvalues of P. From Eq. (20.9.15) we can write the limn!1 Pn as follows: P ¼ lim Pn ¼ lim ½MLM1 n ¼ M lim Ln M1 n!1
n!1
(20:9:16)
n!1
As n ! 1, since all except one of the eigenvalues in L are less than 1, they all tend to 0 as shown below: 3 2 3 2 1 0 0 1 0 0 6 1 l2 0 7 6 0 0 0 7 7 6 7 6 (20:9:17) ¼6. . lim Ln ¼ lim 6 . . .. 7 .. 7 n!1 n!14 . .5 . 5 4 .. .. . .. 0
0
0
lN
0
0
It can be shown that for a stochastic matrix with distinct thepeigenvector corpffiffiffiffi eigenvalues, pffiffiffiffi ffiffiffiffi responding to the eigenvalue 1 is given by m ¼ ½1= N , 1= N , . . . , 1= N T . Substituting Eq. (20.9.17) in Eq. (20.9.16) and expanding, we obtain P ¼ lim Pn ¼ P1 n!1
2 1 pffiffiffiffi m01 6 N 6 6 1 6 pffiffiffiffi m11 6 ¼6 N 6 . .. 6 . . 6 . 4 1 pffiffiffiffi mN1 N
m0N
3
72 7 1 0 7 6 m1N 7 76 0 0 76 4 .. 7 7 . 7 0 0 5 mNN
0
32 m0 00 0 6 m0 7 0 76 10 76 . 56 4 .. 0
m0N0
m001 m011 .. . m0N1
3 m00N m01N 7 7 .. 7 7 . 5 m0NN
(20:9:18) 21
0
where the primes on m denote the elements of the inverse matrix M ¼ M of the modal matrix M. Equation (20.9.18) can be simplified as 3 3 2 2 0 1 m00 m001 m00N pffiffiffiffi p ffiffiffiffi p ffiffiffiffi p ffiffiffiffi 6 N7 6 N N N7 7 7 6 6 6 1 7 6 m0 0 0 7 m01 m0N 7 6 pffiffiffiffi 7 6 p00 6 N 7 0 6 ffiffiffiffi pffiffiffiffi pffiffiffiffi 7 0 0 7 6 N N7 (20:9:19a) P¼6 7 6 . 7 m00 m01 m0N 6 N 6 . 7 6 .. .. 7 .. 6 . 7 6 . . 7 . 7 7 6 6 0 4 1 5 4 m00 m001 m00N 5 pffiffiffiffi pffiffiffiffi pffiffiffiffi pffiffiffiffi N N N N or
2
p0 6 p0 6 P¼6 . 4 .. p0
p1 p1 .. . p1
3 pN pN 7 7 .. 7 . 5 pN
(20:9:19b)
is the steady-state probability transition matrix. Note that in the P matrix each column has the same elements. Substitution of Eq. (20.9.19b) in Eq. (20.9.14) results in lim p(n) ¼ p(0) P1 ¼ p(0) P ¼ ½ p0
n!1
p1
pN ¼ p
(20:9:20)
This equation shows that the limiting state occupancy probabilities are independent of the initial state probability row vector p(0).
540
Classification of Random Processes
Example 20.9.1 A two-state Markov chain with probability transition matrix P as in Eq. (20.9.5) is shown below along with the initial probability row vector p(0): 1a a P¼ ; p(0) ¼ p(0) ¼ ½ p 1p p(0) 0 1 b 1b We want to find the equilibrium probability distributions of the occupancy of states 1, 21 (0) and show that they are independent of the initial occupancy p(0) 0 and p1 . The eigenvalues of P are obtained by solving the characteristic equation given by l 1 þ a a ¼ (l 1)(l 1 a þ b) ¼ 0 jlI Pj ¼ b l 1 þ b and the eigenvalues l1 ¼ 1 and l2 ¼ 1 2 a 2 b. The unnormalized eigenvector c1 corresponding to l1 ¼ 1 is obtained from the eigenequation a a c21 c11 1 ¼ 0 or ¼ b b c22 c12 1 and the unnormalized eigenvector c2 corresponding to l2 ¼ 1 2 a 2 b is obtained from
b b
a a
c21 c22
¼0
or
c21 c22
"
¼
1 b a
#
The matrices of unnormalized eigenvectors MU and its inverse M21 U are 2 3 b a " # 6bþa bþa7 1 1 6 7 b MU ¼ ¼6 M1 7 U 1 4 a a 5 a bþa bþa The diagonal eigenvalue matrix is L ¼ n
(n)
lim P(n)
0
0 1ab 1 0 0 0 .
, and since 1 2 a 2 b , 1, the limit as
¼ Hence, as n ! 1, P n is given by 2 3 2 3 b a b a " # 6 7 6 7 1 1 1 0 6 bþa bþa 7 6 bþa bþa 7 b ¼ 6 7¼6 7 0 0 4 a 1 a 5 4 a a 5 a bþa bþa bþa bþa
n ! 1 of L is given by limn!1 L
n!1
1
and 2
(1)
p¼p
(0) 1
¼p P ¼½p
b 6b þ a 6 1 p 6 4 b bþa
a 3 b þ a7 b 7 7¼ bþa a 5 bþa
which is independent of the initial probability vector p (0) ¼ [ p
a bþa
1 2 p].
Example 20.9.2 A book representative visits three universities a, b, and c each week. However, there is a finite probability that she may stay over for another week in any of the three places. The transition probability matrix P and the initial probability row
20.9
Markov Chains
541
FIGURE 20.9.2
vector p (0) of being in states a, b, and c at time 0 are shown below: 2 a a 0:2 6 P ¼ b 4 0:1
b 0:15 0:4
c 3 0:65 7 0:5 5
c
0:25
0:05
0:7
2
p(0)
3T
3T 0:3 6 7 6 7 ¼ 4 p(0) b 5 ¼ 4 0:45 5 0:25 p(0) c p(0) a
2
We have to find (1) the probabilities of the representative being in the universities a, b, and c after 3 weeks and (2) the steady-state probability of being in any of the universities. The state transition flow diagram for this three-state Markov chain is shown in Fig. 20.9.2. 1. We can apply Eq. (20.9.14) to obtain the probabilities that she will be in the various universities after 3 weeks. Thus 2
p(3) a
3T
2
0:3
3T 2
0:2
0:15
0:65
33
6 7 6 7 6 7 (0) 3 p(3) ¼ 4 p(3) 0:4 0:5 5 b 5 ¼ p P ¼ 4 0:45 5 4 0:1 0:25 0:7 0:25 0:05 p(3) c 2 3T 2 3 2 3 0:3 0:293 0:237 0:47 0:347 6 7 6 7 6 7 ¼ 4 0:45 5 4 0:315 0:254 0:431 5 ¼ 4 0:251 5 0:25 2. The eigenvalues of the matrix P are The modal matrix M is: 2 0:57735 M ¼ 4 0:57735 0:57735
0:47
0:262
0:268
0:402
l1 ¼ 1, l2 ¼ 0.23197, l3 ¼ 20.58197. 0:37197 0:89737 0:22574
3 0:56601 0:32757 5 0:75652
and P1 ¼ML1 M1 2 32 32 3 0:57735 0:37197 0:56601 100 0:63437 0:43479 0:66288 6 76 76 7 ¼ 4 0:57735 0:89737 0:32757 54 0 0 0 54 0:65635 0:80072 0:14437 5 0:57735 0:22574 0:75652 000 0:67997 0:0929 0:77287 2 3 0:36626 0:25103 0:38272 6 7 ¼ 4 0:36626 0:25103 0:38272 5 0:36626 0:25103 0:38272
542
Classification of Random Processes
FIGURE 20.9.3
and the steady-state probability p ¼ p(1) ¼ p(0)P ¼ [0.36626 0.25103 0.38272]. The evolution of state occupancy probabilities p(n) for all three states are shown in Fig. 20.9.3 for n ¼ 1, 2, ... ,12. From the figure it is seen that steady-state equilibrium has been obtained in about 9 weeks. Example 20.9.3 A new automobile can be classified into five states: 1 ¼ excellent, 2 ¼ good, 3 ¼ fair, 4 ¼ poor, 5 ¼ scrap. The transition probabilities of going from one state to another in each year is given by the transition probability matrix P. Clearly, the initial probability row vector p will be given by p ¼ ½ 1 0 0 0 0 . At the end of the first year the probabilities of the automobile being in excellent condition (state 1) ¼ 0.6, in good condition (state 2) ¼ 0.25, in fair condition (state 3) ¼ 0.1 and in poor condition (state 4) ¼ 0.05. Similarly, if the automobile is in scrap condition (state 5), the probabilities of it continuing to be in scrap condition ¼ 0.85, in poor condition ¼ 0.1, and in fair condition ¼ 0.05. We will find the probabilities of the condition of the car in (1) 5 years and (2) steady-state equilibrium for the state occupancy probabilities: 2 1 1 0:6 26 6 0:2 P ¼ 36 6 0:05 44 0 5 0
2 0:25 0:45 0:15 0:05 0
3 0:1 0:2 0:4 0:15 0:05
4 0:05 0:1 0:25 0:35 0:1
The state transition flow diagram is shown in Fig. 20.9.4.
FIGURE 20.9.4
5 3 0 0:05 7 7 0:15 7 7 0:45 5 0:85
20.9
Markov Chains
543
1. From Eq. (20.9.13) the state probabilities after n ¼ 5 years is given by p(5) ¼ p(0) P5 , where p(0) is the row vector p(0) ¼ ½ 1 0 0 0 0 . Hence
p(5)
2 3T 2 0:2034 1 6 0 7 6 0:1506 6 7 6 6 7 6 7 6 ¼ p(0) P5 ¼ 6 6 0 7 6 0:0863 6 7 6 4 0 5 4 0:0419 0:0204 0 2 3T 0:2034 7 6 6 0:1984 7 7 6 7 ¼6 6 0:1736 7 7 6 4 0:1502 5 0:2744
0:1984 0:1618
0:1736 0:1502 0:1641 0:1571
0:1090
0:1447 0:1615
0:0656 0:0415
0:1215 0:1572 0:1061 0:1518
3 0:2744 0:3664 7 7 7 0:4985 7 7 7 0:6138 5 0:6802
Thus, after 5 years there is a probability of 0.2034 that the car is still in excellent condition and a probability of 0.2744 that the car is in scrap condition. It is also interesting that with the given transition probabilities, there is a better chance that the car is in excellent condition than in either good, fair, or poor condition. We will now check what happens to these probabilities after a long period of time has elapsed. 2. We follow the procedure of finding the eigenvalues and eigenvectors of the transition matrix P. The eigenvalue matrix L, the corresponding modal matrix of P, and its inverse are as follows: 2
1 60 6 6 L¼6 60 6 40 0
0 0:4529
0 0
0 0
0 0
0
0:2417
0
0
0 0
0 0
2
0:4472 0:7139 6 6 0:4472 0:1188 6 M¼6 6 0:4472 0:6081 6 4 0:4472 0:2899 0:4472 2 M1
0:1114
6 6 0:4614 6 ¼6 6 0:3828 6 4 0:5697 0:0048
0:1496
0:1555
0:7999 0 0 0:1555
3 7 7 7 7 7 7 5
0:4491 0:8065
0:7891 0:5471
0:2789 0:2566
0:2238 0:0293
0:0651
0:1647
0:2695
0:1919 0:5895 0:7069 0:0846
0:3446 0:5495 0:7773
0:5102
0:2363
0:0127
0:1395
0:6005
0:9562
2
1 60 6 6 1 L ¼6 60 6 40 0 3 0:1265 7 0:4190 7 7 0:7403 7 7 7 0:5099 5 0:0201
1:3551
3
7 7 7 7 7 7 1:3288 5 0:4999 0:8695 0:5378
0 0 0 0
0 0
0 0
0
0 0 0 0
0 0
3 0 07 7 7 07 7 7 05 0
544
Classification of Random Processes
Hence from Eq. (20.9.18) we have 2
P ¼ P1 ¼ ML1 M1
0:0499 6 0:0499 6 ¼6 6 0:0499 4 0:0499 0:0499
0:0695 0:0695 0:0695 0:0695 0:0695
0:1205 0:1205 0:1205 0:1205 0:1205
0:1541 0:1541 0:1541 0:1541 0:1541
3 0:6060 0:6060 7 7 0:6060 7 7 0:6060 5 0:6060
and the steady-state probabilities row vector p is p ¼ p(0) P ¼ ½ 0:0499 0:0695
0:1205 0:1541
0:6060
These are more reasonable numbers since the long-term probabilities of being in various states are excellent 4.99%, good 6.95%, fair 12.05%, poor 15.41%, and finally scrap 60.6%. The conclusion is that there is 60.6% chance that the automobile will be a scrap in the long run with the given probability transition matrix. First-Passage Probabilities and Classification of States
A Markov chain is called irreducible if every state can be reached from every other state in a finite number of steps. In this case, the probability of going from every state i to every other state j in the state space S of the chain will be given by the probability, p(m) ij . 0, where m is a finite integer. In this case, the states are called intercommunicating. On the other hand, if E is a proper subset of S, E , S, and if i and j are states in E, then the subset E is closed if X
pij ¼ 1
for all
i[E
j[E
In other words, no transition is possible from a state i [ E to a state k [ E c. In this case the Markov chain is called reducible. If there are no other proper subsets of E that are closed, then it is called an irreducible sub-Markov chain. If i [ E is the only state, then it is called an absorbing state, in which case pii ¼ 1. Let us consider that the Markov chain is initially in state i at time n. We define the probability of first return to i at time n þ m as fii(m), or fii(m) ¼ P{Xnþ1 = i, . . . , Xnþm1 = i, Xnþm ¼ i j Xn ¼ i}
(20:9:21)
with one-step transition fii(1) ¼ P{Xnþ1 ¼ i j Xn ¼ i} ¼ p(1) ii ¼ pii similar to Eq. (20.9.2). For a homogeneous Markov chain, Eq. (29.9.21) can also be written as fii(m) ¼ P{X1 = i, . . . , Xm1 = i, Xm ¼ i j X0 ¼ i}
(20:9:22)
with one-step transition fii(1) ¼ P{X1 ¼ i j X0 ¼ i} ¼ p(1) ii ¼ pii . In a similar manner we can also define the probability of first passage from state i at time n to state j at time n þ m as fij(m) , or fij(m) ¼ P{Xnþ1 = j, . . . , Xnþm1 = j, Xnþm ¼ j j Xn ¼ i}
(29:9:23)
20.9
Markov Chains
545
with one-step transition fij(1) ¼ P{Xnþ1 ¼ j j Xn ¼ i} ¼ p(1) ij ¼ pij in conformity with Eq. (20.9.2). For a homogeneous Markov chain, Eq. (20.9.23) is equivalent to fij(m) ¼ P{X1 = j, . . . , Xm1 = j, Xm ¼ j j X0 ¼ i}
(20:9:24)
with the corresponding one-step probability fij(1) ¼ P{X1 ¼ j j X0 ¼ i} ¼ p(1) ij ¼ pij . The probabilities of eventual first return to state i or the first passage to state j starting from state i are given by
fi ¼
1 X
fii(m)
fij ¼
m¼1
1 X
fij(m)
(20:9:25)
m¼1
It is now possible to have more classifications of the Markov chains depending on the nature of fi. If fi ¼ 1, the state i is called recurrent; if fi , 1, the state i is called transient. If the chain starts at time 0 in state i and returns to state i only at times a, 2a, . . . , with period a . 1, then the state i is called periodic with period a. If a ¼ 1, then i is aperiodic. If fi ¼ 1 and fij ¼ 1, we can define a mean recurrence time and a mean first-passage time from state i to state j as follows:
mi ¼
1 X
mfi
mij ¼
m¼1
1 X
mfij
(20:9:26)
m¼1
With this knowledge we can classify the states even further. If the mean recurrence time mi is finite, then the state is called positive recurrent; if it is infinite, it is called null recurrent. Finally, a state i is ergodic if it is aperiodic and positive recurrent, or a ¼ 1, fi ¼ 1 and mi , 1. A finite-state aperiodic irreducible Markov chain is always ergodic, and the occupancy probability of the states as n ! 1 will tend to an equilibrium distribution independent of the initial probabilities as in Examples 20.9.1 – 20.9.3. In transient states the equilibrium distribution will be 0.
Example 20.9.4
The transition matrix P of a four-state Markov chain is given below: 1 8 2 1 1 > > > <26 0 6 P¼ 6 > 3 > 4 0 > : 4 0:3
2 0 0:4
3 0 0:6
0:8 0
0:2 0:2
4 3 0 0 7 7 7 0 5 0:5
and the state transition flow diagram is shown in Fig. 20.9.5. We can obtain the classification of the states by inspection of the transition flow diagram of Fig. 20.9.5. From the flow diagram state 1 is absorbing because p11 ¼ 1, and once the transition occurs to state 1, it never gets out of it. It is also a recurrent state. States 2 and 3 are recurring states because the transition occurs only between these two states. If the transition occurs out of state 4, it can never get back in. Hence, state 4 is a transient state. We will determine the equilibrium probabilities P from
546
Classification of Random Processes
FIGURE 20.9.5
Eq. (20.9.18). The diagonal eigenvalue matrix L is given by 2
0:5 60 L¼6 40 0
0 1 0 0
0 0 0:4 0
3 0 07 7 05 1
2
0 60 n lim L ¼ 6 40 n!1 0
0 1 0 0
3 0 07 7 05 1
0 0 0 0
The presence of two 1s for eigenvalues indicates the presence of sub-Markov chains. The modal matrix and its inverse are 2
0
0
0
0:8575
3 7 7 7 5
60 6 M¼6 40
0:6804 0:5907 0:6804 0:7877
0 0
1
0:2722 0:1750
0:5145
2
0:6
6 0 6 M1 ¼ 6 4 0 1:1622
0:3556
0:0444
0:8398
0:6299
0:7255 0
0:7255 0
1
3
07 7 7 05 0
and the equilibrium probability P is given by
P ¼ M lim Ln M1 n!1
1 2 8 2 1 1 0 > > > 6 < 2 6 0 0:5714 ¼ 6 > > 3 4 0 0:5714 > : 4 0:6 0:2286
3
4
0
0
3
0:4286 0 7 7 7 0:4286 0 5 0:1714 0
State 4 is transient since p4j ¼ 0, j ¼ 1, . . . , 4. State 1 is absorbing since p11 ¼ 1. States 2 and 3 are recurrent with p23 ¼ 0.4286 and p32 ¼ 0.5714.
20.9
Markov Chains
547
Example 20.9.5 We will slightly change the transition probabilities by introducing a path between states 2 and 1 by inserting p21 ¼ 0.02 and modifying p23 ¼ 0.58: 1 2 8 2 1 1 0 > > > 6 < 2 6 0:02 0:4 P¼ 6 > 340 0:8 > > : 4 0:3 0
3 0
4 3 0 0:58 0 7 7 7 0:2 0 5 0:2 0:5
The state transition flow diagram is shown in Fig. 20.9.6. This small change makes a big difference in the properties of the Markov chain. From the flow diagram, the only state that is recurrent is 1. The other states (2 –4) are transient because everything flows into state 1 directly or indirectly. This will be confirmed from the equilibrium probabilities P of the states. The diagonal eigenvalue matrix L and the limn!1 are given by 2
0:5 60 L¼6 40 0
0 0:9885 0 0
0 0 0:3885 0
2
3 0 07 7 05 1
0 60 n lim L ¼ 6 40 n!1 0
0 0 0 0
0 0 0 0
3 0 07 7 05 1
Since there is only one eigenvalue of 1, there are no sub-Markov chains. The modal matrix and its inverse are 2
0 60 M¼6 40 1
3 0 0 0:5 0:6739 0:5830 0:5 7 7 0:6837 0:7926 0:5 5 0:28 0:1784 0:5
2
3 0:5853 0:3687 0:0461 1 6 1:4748 0:8497 0:6251 0 7 7 M1 ¼ 6 4 0:0106 0:7330 0:7225 0 5 2 0 0 0
FIGURE 20.9.6
548
Classification of Random Processes
and the equilibrium probability P is given by 1 2 1 1 26 61 P ¼ M lim Ln M1 ¼ 6 n!1 341
2 3 4 3 0 0 0 0 0 07 7 7 0 0 05
4 1 0 0 0 The equilibrium probability matrix shows that states 2 –4 are transient because the probabilities are zero. State 1 is the absorbing recurrent state. Example 20.9.6
A state transition matrix P for a seven-state Markov chain is given by
1 8 2 1 > > > 6 0:6 > > 2 6 0:7 > > > 6 > > 6 > <360 6 P ¼ 460 > 6 > > 6 > >560 > > 6 > > 6 4 0:2 > > : 7 0
2
3
4
5
6
7
0:4
0
0
0
0
0
0:3 0
0 0:7
0 0 0:3 0
0 0
0 0
0 0 0 0
0:4 0:5 0:1 0:3
0:3 0:4 0:1 0:2
0:3 0:1 0:1 0:4
3
7 7 7 7 7 7 0 0 7 7 0 0 7 7 0:2 0:3 7 5 0:1
The corresponding state transition flow diagram is shown in Fig. 20.9.7.
FIGURE 20.9.7
20.9
Markov Chains
549
We will classify the states by inspection from Fig. 20.9.7. The flow diagram shows that states 1 and 2 form a sub-Markov chain since there are no paths out of that pair. Hence they form recurring states. States 3– 5 form another sub-Markov chain, and there are no paths leading out of these three states. Hence they form another set of recurring states. All paths lead out of states 6 and 7 and hence are transient states. We can confirm these findings by computing the equilibrium probability P. The diagonal eigenvalue matrix L and the limn!1 Ln are given by 2
0:2
60 6 6 60 6 6 L ¼ 60 6 60 6 6 40 0
0
0
0
0
0
0:1 0
0 1
0 0
0 0
0 0
0 0
0 0
0:2303 0 0 0:1303
0 0
0
0
0
0
1
0
0
0 2
0
0
0 60 6 6 60 6 6 n lim L ¼ 6 0 n!1 6 60 6 6 40
0
3
7 7 7 7 7 7 0 7 7 0 7 7 7 0 5 0:1 3 0 07 7 7 07 7 7 07 7 07 7 7 05 0 0
0
0
0
0 0
0 0
0 1
0 0
0 0 0 0
0
0
0
0 0
0 0
0 0
0 0
0 0 0 1
0 0
0
0
0 0
0
Since there are two eigenvalues of 1, we conclude that there is a sub-Markov chain. The modal matrix M and its inverse M 21 are given by 2
0
0
0
0
0
0:6963 0:4710
0
0
0
0
0:6963
60 6 6 60 6 6 M ¼ 60 6 60 6 6 41
0 0
0:4682 0:028 0:4682 0:0438
0:1665 0:4607
0 0
0 0:9487
0:4682 0:3511
0:0271 0:9946
0:4388 0:4821
0 0:1741
0
0:3162
0:4682
0:0861
0:5789
2
M
1
0:0833 6 0 6 6 6 0 6 6 ¼6 0 6 6 0 6 6 4 0:9139 0:7720
0
18:25 2:1082
8:75 4:2164
13:25 1:0541
0
1:2521
0:6629
0:2210
0 0
16:6314 0:3084
5:0356 1:0184
11:5958 1:3268
0:3333 0
0:5222 0:7720
0 0
0 0
0 0
3
0:8243 7 7 7 7 0 7 7 0 7 7 7 0 7 7 0:3140 5 0 3 1 3 0 3:1623 7 7 7 7 0 0 7 7 0 0 7 7 7 0 0 7 7 0 0 5 0 0
550
Classification of Random Processes
The equilibrium probability P is given by
P ¼ M lim Ln M1 n!1
1 8 2 1 0:6364 > > > > > 26 > 6 0:6364 > > > 6 > 6 > <360 6 ¼ 460 > 6 > >560 > > 6 > > 6 > > 6 4 0:1591 > > : 7 0
2
3
4
5
6
7
0:3636
0
0
0
0
0
0:3636 0
0 0:5862
0 0:3103
0 0 0:1035 0
0
0:5862
0:3103
0:1035 0
0 0:0909
0:5862 0:4396
0:3103 0:2328
0:1035 0 0:0776 0
0
0:5862
0:3103
0:1035 0
3
07 7 7 07 7 7 07 7 07 7 7 05 0
The matrix P shows that there are two closed sets of recurring states f1,2g and f3,4,5g with equilibrium probabilities. States 6 and 7 have zero probabilities, thus confirming that they are transient states. An interesting point to note is that there is no direct transition from state 6 to state 2 in the state transition matrix but there is a direct transition from state 6 to state 2 in the equilibrium probability distribution.
Properties of Markov Chains 1. Let X ¼ fXn: n ¼ 0, 1, . . . , Ng be a Markov chain with state space S ¼ fi, j, k, . . .g. The m-step transition probability p(m) ij (n) is the probability of Xnþ1 ¼ j given that Xn¼i, or P{Xnþm ¼ jjXn ¼ i} ¼ P{Xnþm ¼ jjXn ¼ i} ¼ p(m) ij (n) The Markov chain is homogeneous when P{Xnþm ¼ jjXn ¼ i} ¼ P{Xm ¼ jjX0 ¼ i} ¼ p(m) ij The transition probabilities are independent of time n. 2. If P is the matrix of one-step transition probabilities, then pij is the (i, j)th element in P as shown below: 0 1 ... 8 2 p00 p01 > > >06 > > p p11 10 > > 16 > 6 . > .. > < 6 6 .. . 6 P¼ 6 > > i 6 pi0 pi1 > > 6 > .. > 6 .. > > 4 . . > > :N pN0 pN1
j
... N
p0j p1j .. .
pij .. . pNj
p0N p1N .. .
3
7 7 7 7 7 7 piN 7 7 .. 7 7 . 5 pNN
P is called the stochastic matrix or Markov matrix with row sum equal to 1, or N X pij ¼ 1, i ¼ 0, 1, . . . , N j¼0
3. The Markov chain satisfies the Chapman –Kolmogorov equation X X P{Xm ¼ jjXm1 ¼ k}P{Xm1 ¼ kjX0 ¼ i} ¼ pkj p(m1) p(m) ij ¼ ik k
k
20.10 Martingale Process
551
4. Let the row of the initial probability of the states of a Markov chain at time 0 vector (0) (0) be p(0) ¼ p(0) , p , . . . , p . Then the row vector of state occupancy probabilities N 0 1 (n) (n) , p , . . . , p at time n, p(n) ¼ p(n) N , is given by 0 1 p(n) ¼ p(n1) P ¼ p(0) Pn 5. A Markov chain with Markov matrix P is called irreducible if every state can be reached from every other state in a finite number of steps. The long-term statistical equilibrium occupancy probabilities p of the states can be obtained from solving p ¼ pP
or p(I P) ¼ 0
6. Only one eigenvalue of an irreducible Markov chain is 1, and the absolute values of all other eigenvalues are less than 1. 7. Starting from a state i at time n, the probability of first return to i at time n þ m as fii(m) is defined by fii(m) ¼ P{Xnþ1 = i, . . . , Xnþm1 = i, Xnþm ¼ ijXn ¼ i} and starting from a state i at time n the probability of first passage to j at time n þ m as fij(m) is defined by fij(m) ¼ P{X1 = j, . . . , Xm1 = j, Xm ¼ jjX0 ¼ i} The probabilities of eventual first return to state i or first passage to state j are fi ¼
1 X
fii(m)
m¼1
fij ¼
1 X
fij(m)
m¼1
If fi or fij is equal to 1, then the state i or j is certain, and both are called recurrent. If they are less than 1, then the states are called transient. 8. The mean recurrence time and the mean first passage time can be defined as mi ¼
1 X
mfi
m¼1
mij ¼
1 X
mfij
m¼1
If mi , 1, then the state i is positive recurrent, and if mi ¼ 1, then i is null recurrent and similar definitions apply to state j if mij , 1, or mij ¼ 1 9. In a Markov chain with transitionP matrix P, if E is a proper subset of the state space S, or if E , S, then E is closed if j[E pij ¼ 1 for all i [ E. The states in E form a sub-Markov chain in which case there will be two eigenvalues of 1. 10. A closed set of states E , S containing no proper subsets that are closed is called irreducible. If the number of states within an irreducible set is finite, then each state in E is recurrent. For any irreducible set, every state communicates with every other state. 11. If all the states of a Markov chain are irreducible, then a steady-state row vector defined by p can be determined from p ¼ pP, or p(I P) ¼ 0.
B 20.10
MARTINGALE PROCESS
The meaning of a martingale in both British and American dictionaries refers to a strap preventing a horse from rearing. This has no relevance in the mathematical sense,
552
Classification of Random Processes
where it conveys the sense of a fair game. It is a stochastic process where the best estimate of the future value conditioned on the past, including the present, is the present value. It is a process without trend and hence unpredictable. Martingale concepts are widely used in engineering and stochastic finance. Many filtering problems in engineering such as the Kalman filter [27] can be cast in a martingale framework. The option pricing model developed by Black and Scholes [7] in 1973 for pricing stock options can be formulated within the framework of martingale theory. Just like a Markov process, conditional expectations play a key role in martingale processes. For detailed study of martingales, see Refs. 16, 26, 35, 37, and 40. A random sequence fYn, n 0g is a discrete martingale with respect to the sequence fXm, m ng if the following conditions are satisfied: (1) EjYn j , 1 (2) E ½Yn jXm , Xm1 , . . . , X0 ¼ Ym ; m n
(20:10:1)
If the second condition in Eq. (20.10.1) is modified as (2a) E ½Yn jXm , Xm1 , . . . , X0 Ym , m n
(20:10:2)
then the random sequence fYn, n 0g is a submartingale with respect to the sequence fXm, m 0g and if modified as (2b) E ½Yn jXm , Xm1 , . . . , X0 Ym , m n
(20:10:3)
then the random sequence fYn, n 0g is a supermartingale with respect to the sequence fXm, m 0g. A random process fY(t), t 0g is a continuous martingale with respect to the process fX(s), 0 s , tg if the following conditions are satisfied: (3) EjY(t)j , 1 (4) E½Y(t)jX(s), 0 s , t ¼ Y(s)
(20:10:4)
If the second condition in Eq. (20.10.1) is modified as (4a) E ½Y(t)jX(s), 0 s , t Y(s)
(20:10:5)
then the random process fY(t), t 0g is a continuous submartingale with respect to the process fX(s), 0 s , tg and if modified as (4b) E ½Y(t)jX(s), 0 s , t Y(s)
(20:10:6)
then the random process fY(t), t 0g is a continuous supermartingale with respect to the underlying process fX(s), 0 s , tg. We can explain the concept of a martingale by a simple example. A player tosses a fair coin p ¼ q ¼ 12 and wins a dollar if heads come up and loses a dollar if tails come up. This is the modified Bernoulli process described by Eq. (20.4.11). Let Wn ¼ Z1 þ Z2 þ . . . þ Zn be his fortune at the end of n tosses. The average fortune of the player at the (n þ 1)st toss is the same as his current fortune Wn, and this is not affected by how he arrived at his current fortune, that is, E ½wnþ1 jZn , Zn1 , . . . , Z1 ¼ Wn , thus capturing the notion that the game is fair. In the same spirit we can think of the game being biased in favor of the player (submartingale) or biased against the player (supermartingale). Unless otherwise stated, we will assume that the condition EjYn j , 1 or EjY(t)j , 1 is always satisfied in the following examples.
20.10 Martingale Process
553
Example 20.10.1 Random-Walk Process Let fZig be the modified Bernoulli process described by Eq. (20.4.11) with mZ ¼ p 2 q and s2Z ¼ 4pq. The corresponding random-walk process is a modified form of Eq. (20.4.15): Wn ¼
n X
Zi ¼
n1 X
i¼1
Zi þ Zn ¼ Wn1 þ Zn
(20:10:7)
i¼1
The mean value of Wn from Eq. (20.4.16) is mW ¼ n( p 2 q), and the variance of Wn from Eq. (20.4.17) is s2W ¼ 4npq. The conditional expectation E ½Wnþ1 jZn , Zn1 , . . . , Z1 is given by E ½Wnþ1 jZn , Zn1 , . . . , Z1 ¼ E ½Znþ1 þ Wn jZn , Zn1 , . . . , Z ¼ ( p q) þ Wn
(20:10:8)
If we now define Yn ¼ Wn 2 n( p 2 q) in Eq. (20.10.5), we can write E ½Ynþ1 jZn , Zn1 , . . . , Z1 ¼ E ½Znþ1 ( p q) þ Yn jZn , Zn1 , . . . , Z ¼ Yn
(20:10:9)
Hence Yn is a discrete martingale with respect to the sequence fZk, 1 k ng. Example 20.10.2 The characteristic function of Wn in Eq. (20.10.4) is FWn (v) ¼ E ½ejvWn , and we define Yn ¼
e jvWn FWn (v)
and
E ½Yn ¼ 1
For m n, we can write for the following conditional expectation: jv(Wn Wm ) jvWm e e Wm , . . . ,W1 E ½Yn jWm , . . . ,W1 ¼ E FWn (v) Because of the independent increment property, (Wn 2 Wm) is independent of Wm and hence E ½e jv (Wn Wm ) jWm , . . . , W1 ¼ E ½e jv (Wn Wm ) and the preceding equation can be written as E ½Yn jWm , . . . ,W1 ¼
E ½e jv(Wn Wm ) e jvWm e jvWm ¼ ¼ Ym , FWnm (v) FWm (v) FWm (v)
mn
Hence Yn is a martingale with respect to the sequence fWm, . . . , W1g. Example 20.10.3 The sequence fZig of Example 20.10.2 is now a set of i.i.d. random variables with p ¼ q ¼ 12 with variance s2Z. Here the mean value mZ ¼ 0. We form the sequence !2 n X Yn ¼ Zi ns2Z i¼1
Since E(
Pn
i¼1
2
ns2Z ,
we have E[Yn] ¼ 0. For m n, we can write 82 3 !2 nm < X Z s2Z (n m)5 E ½Yn jZm , . . . , Z1 ¼ E 4 : i¼m i
Zi ) ¼
2 þ4
m X i¼1
!2 Zi
9 3 = ms2Z 5Zm , . . . , Z1 ;
554
Classification of Random Processes
Pnm
2
s2Z (n m) is independent of the sequence fZm, . . . , Z1g we have 2 3 2 3 !2 !2 nm nm X X E4 Zi s2Z (n m)Zm , . . . , Z1 5 ¼ E4 Zi s2Z (n m)5 ¼ 0 i¼m i¼m
Since E
i¼m
Zi
and hence 2 E ½Yn jZm , . . . , Z1 ¼ E4
m X i¼1
!2 Zi
3 !2 m X 2 msZ Zm , . . . , Z1 5 ¼ Zi ms2Z ¼ Ym i¼1
and Yn is a discrete martingale with respect to the sequence fZk, 1 k ng. Example 20.10.4 (Poisson Martingale) The Poisson process fN(t), t 0g with mean mN(t) ¼ lt is given by the probability function P{N(t) ¼ k} ¼ elt
(lt)k k!
Let Y(t) ¼ N(t) 2 lt. We will show that Y(t) is a martingale. For s t we can write E ½Y(t) j Y(s), s t ¼ E ½Y(t) Y(s) þ yðsÞ j Y(s), s t Since Y(t) is an independent increment process, Y(t) 2 Y(s) is independent of Y(s). Hence E ½Y(t) Y(s) j Y(s), s t ¼ E ½Y(t) Y(s), s t ¼ 0 Hence we have E ½Y(t)jY(s) ¼ Y(s),
st
showing that Y(t) is a martingale with respect to the process fY(s), s tg. Example 20.10.5 (Wiener Martingale) The Wiener process fW(t), t 0g has been defined in Section 20.7 as a zero mean independent increment process with density function given by Eq. (20.7.1): 1 2 2 fW (w, t) ¼ pffiffiffiffiffiffiffiffiffiffi eð1=2Þ(w =s t) 2pst Using the same procedure as in previous examples, we can write E ½W(t)jW(s), s t ¼ E ½W(t) W(s) þ W(s)jW(s), s t Since W(t) is an independent increment process, W(t) 2 W(s) is independent of W(s). Hence E ½W(t) W(s)jW(s), s t ¼ E ½W(t) W(s), s t ¼ 0 Hence we have E ½W(t)jW(s) ¼ W(s),
st
showing that W(t) is a martingale with respect to the process fW(s), s tg. In all the five examples given above the underlying random process was an independent increment process. The question arises as to whether all independent increment processes with zero mean are martingales. We will show that this is indeed the case. If
20.10 Martingale Process
555
X(t) is an independent increment process, then from Eq. (20.4.1), we have E{½X(ti ) X(ti1 )½X(tiþ1 ) X(ti )} ¼ E ½X(ti ) X(ti1 )E ½X(tiþ1 Þ X(ti )
(20:4:1)
If the mean of X(t) is mX(t), then we can define a zero mean independent increment process Y(t) ¼ X(t) 2 mX(t) and write the conditional expectation E ½Y(t)jY(s) for 0 s t as follows: E ½Y(t) j Y(s) ¼ E ½Y(t) Y(s) j Y(s) þ E ½Y(s) j Y(s),
0st
(20:10:10)
Using the independent increment property, we obtain E ½Y(t) Y(s) j Y(s) ¼ E ½Y(t) Y(s) ¼ 0,
0st
(20:10:11)
0st
(20:10:12)
Substituting Eq. (20.10.11) in Eq. (20.10.10), we obtain E ½Y(t) Y(s) j Y(s) ¼ E ½Y(t) Y(s) ¼ 0,
thus showing that a zero mean independent increment process is a martingale with respect to the process fY(s), 0 s tg. However, not all martingales are independent increment processes, as shown in the following example. Example 20.10.6 (Likelihood Ratio Martingale) The likelihood ratio under the two hypotheses H0 and H1 in Eq. (18.6.17) is modified as LX (n) ¼
fX (Xn , . . . , X1 j H1 ) fX (Xn , . . . , X1 j H0 )
We will show that under the hypothesis H0, the likelihood ratio LX(n) is a martingale with respect to the sequence fXn, . . . , X1g. The conditional expectation E[LX(n þ 1)jXn, . . . , X1jH0] can be written as E ½LX (n þ 1) j Xn , . . . , X1 j H0 ð1 fX (xnþ1 , xn , . . . , x1 j H1 ) fX (xnþ1 , xn , . . . , x1 j H0 )dxnþ1 ¼ f 1 X (xnþ1 , xn , . . . , x1 j H0 ) From the definition of conditional probabilities, we can substitute the following in the equation above fX (xnþ1 , xn , . . . , x1 j H0 ) ¼ fX (xnþ1 j xn , . . . , x1 j H0 ) fX (xn , . . . , x1 j H0 ) and write E ½LX (n þ 1) j Xn , . . . , X1 j H0 ð1 fX (xnþ1 , xn , . . . , x1 j H1 ) fX (xnþ1 j xn , . . . , x1 j H0 ) dxnþ1 ¼ fX (xn , . . . , x1 j H0 ) fX (xnþ1 j xn , . . . , x1 j H0 ) 1 ð1 1 ¼ fX (xnþ1 , xn , . . . , x1 j H1 )dxnþ1 ¼ LX (n) fX (xn , . . . , x1 j H0 ) 1
556
Classification of Random Processes
thus showing that LX (n) is a martingale with respect to the sequence fXn, . . . , X1g under the hypothesis H0. Example 20.10.7 The Wiener process fW(t), t 0g has mean 0 and variance s2t. We form the function Y(t) with W(t) as Y(t) ¼ ekW(t)k
2
(s2 t=2)
t.0
,
where k is an arbitrary constant. We will show that Y(t) is a martingale with respect to the Wiener process fW(t), t 0g. We form the conditional expectation with s t: E ½Y(t) j W(s), 0 s , t ¼ ek
2
(s2 t=2)
E½ekW(t) j W(s)
¼ ek
2
(s2 t=2)
E ½ek½W(t)W(s) eW(s) j W(s)
Using the independent increment property E ½ek½W(t)W(s) jW(s) ¼ E ½ek½W(t)W(s) in the preceding equation, we find that E ½Y(t) j W(s), 0 s , t ¼ ekW(s) ek
2
(s2 t=2)
E½ek½W(t)W(s)
But E ½ek½W(t)W(s) is the moment generating function (MGF) of the process W(t) 2 W(s), which has zero mean and variance s2(t 2 s). From Example 11.5.2 the MGF is given by 2 2 E ½ek½W(t)W(s) ¼ ek ½s (ts)=2 , and substituting this equation, we obtain E ½Y(t) j W(s), 0 s , t ¼ ekW(s) ek
2
ðs2 t=2) k2 ½s2 ðtsÞ=2
e
¼ e kWðsÞ ek
2
ðs2 s=2Þ
¼ Y(s)
thus showing the required result. Example 20.10.8 The random process is given by Y(t) ¼ W 2(t), where W(t) is a Wiener process with variance s2t. We will show that Y(t) is a submartingale. We form the conditional expectation with s , t: E ½Y(t) j W(s) ¼ E ½W 2 (t) W 2 (s) W 2 (s) j W(s),
0s,t
With 0 s , t and, using the independent increment property, this equation can be written as E ½Y(t) j W(s) ¼ E ½W 2 (t) W 2 (s) W 2 (s) ¼ s2 (t s) W 2 (s) . Y(s) showing that Y(t) is indeed a submartingale. However, we can show that V(t) ¼ W 2(t) 2 s2t is a martingale as shown below: E ½V(t) j W(s) ¼ E ½W 2 (t) s2 t W 2 (s) s2 s þ V(s) ¼ V(s),
0 s , t
Thus, V(t) is a martingale with respect to the Wiener process fW(t), t 0g. Example 20.10.9 If fYn, n 0g is a martingale with respect to the sequence fXm, m 0g and f (.) is some convex function, then f(Yn) is submartingale with respect to fXm, m 0g as shown below: From Jensen’s inequality [Eq. (14.6.3), E ½h(X) h(E ½X), we can write E ½f(Yn )jXm f{E ½Yn jXm } ¼ f(Ym ) Showing that f(Yn) is a submartingale.
20.11 Periodic Random Process
557
As particular cases the following convex functions of a martingale Yn are all submartingales: (1) jYnj, (2) Y2n, (3) jYnja. Similarly, convex functions of a continuous martingale Y(t) are submartingales as shown below: 1. Poisson martingale N(t) 2 lt. [N(t) 2 lt]2 is a submartingale. 2. Wiener martingale W(t). We have already seen in Example 20.10.8 that W 2(t) is a submartingale. 3. W 2(t) 2 s2t is a martingale. [W 2(t) 2 s2t]2 is a submartingale. A martingale process is based on expected values and a Markov process on conditional probabilities, or E ½X(t) j X(k), 0 k s ¼ X(s) P ½X(t) j X(k), 0 k s ¼ P ½X(t) j X(s)
martingale Markov
Hence a martingale process need not be a Markov process or vice versa. However, all independent increment processes are both martingales and Markov processes. Decomposition of Submartingales
Doob [16] and Meyer [41] have shown that under certain regularity conditions any submartingale can be uniquely decomposed into a martingale and an increasing predictable process. Heuristically, a process A(t) or An is predictable if it can be completely determined from a knowledge of either fX(s), 0 s , t)g or fXk, 0 , k ng. All deterministic functions are predictable. The Doob–Meyer decomposition states that if Yt is a submartingale with respect to an underlying random process fXt, t 0g, then the unique decomposition is given by Yt ¼ Mt þ At
(20:10:13)
where Mt is a martingale with respect to the same underlying random process fXt, t 0g and At is an increasing predictable process. It is the decomposition aspect of the submartingales that gave rise to an elegant proof [19] for the nonlinear filtering problem of which the Kalman linear filtering problem is a special case. Complete details can be found in Refs. 26, 35, and 37.
B 20.11
PERIODIC RANDOM PROCESS
The continuity of the sample paths of a periodic random processes is of concern to us for expanding them in Fourier series. Among the different types of continuity the mean-square continuity is of importance. A random process X(t) is continuous in the mean-square sense if the following condition is satisfied: lim EjX(t) X(s)j2 ! 0 s!t
(20:11:1)
The mean-square continuity of the random process is tied to the autocorrelation RX(t,s) of the process. We will show that a random process X(t) is mean-square continuous if and only if RX(t,s) is continuous at the diagonal point s ¼ t ¼ t: EjX(t) X(s)j2 ¼ RX (t, t) 2RX (t, s) þ RX (s, s)
(20:11:2)
558
Classification of Random Processes
Since RX(t,s) is continuous at s ¼ t, by assumption the righthand side is zero at s ¼ t, thus showing the necessity. Conversely RX (t, t) RX (s, s) ¼ E ½X(t)X(t) E ½Xs2 ¼ E{½X(t) X(s)X(t)} þ E{½X(t) X(s)X(s)} From the preceding equation, using Schwarz’s inequality of Eq. (14.5.13), we can form RX (t, t) RX (s,s) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi E ½jX(t) X(s)j2 E ½X 2 (t) þ E ½jX(t) X(s)j2 E ½X 2 (s)
(20:11:3)
From the assumption of mean-square continuity of X(t) the righthand side goes to zero as t, s ! t and hence RX(t,s) is continuous at s ¼ t. A random process X(t) is periodic in the wide sense with period Tc if mX (t þ kTc ) ¼ mX (t) for all t and integer k RX (t, s) ¼ RX (t þ kTc , s) ¼ RX (t, s þ kTc ) for all t, s, and integer k
(20:11:4)
The autocorrelation function RX(t,s) is periodic in both arguments t and s and is shown in Fig. 20.11.1 in terms of constant-value contours. If X(t) is a stationary process, then with t ¼ t 2 s, Eq. (20.11.4) simplifies to RX (t) ¼ RX (t þ kTc )
for all t and integer k
(20:11:5)
and the mean-square continuity of X(t) guarantees that RX(t) is uniformly continuous in t. Such wide-sense periodic stationary processes are shown in Examples 19.2.1 –19.2.3. As a consequence of the continuity, the Fourier series of RX(t) given by RX (t) ¼
1 X
Rk e jkvc (st) ,
t¼st
(20:11:6)
n¼1
converges uniformly to RX(t). As a consequence of the uniform continuity of RX(t), the zero mean stationary periodic random process X(t) with fundamental frequency vc ¼ 2p/Tc can be represented in
FIGURE 20.11.1
20.11 Periodic Random Process
559
the mean-square sense by a Fourier series 1 X
X(t) ¼
Xn e jnvc t , X0 ¼ 0 and
Xn ¼
n¼1
1 Tc
ð Tc
X(t)ejnvc t dt
(20:11:7)
0
The Fourier coefficients fXng are in general complex random variables, and we will investigate their properties. Since X(t) is a zero mean process, E[Xn] ¼ 0. The crosscorrelation between two of the coefficients can be calculated using Eq. (20.11.7): E ½Xm Xn
1 ¼ 2 Tc 1 ¼ 2 Tc
ð Tc ð Tc 0
E ½X(s)X(t)ejvc (msnt) ds dt
0
ð Tc ð Tc 0
RX (s t)ejvc (msmt) dt ds
(20:11:8)
0
Substituting for RX(s 2 t) from Eq. (20.11.6) in Eq. (20.11.8), we obtain E ½Xm Xn
1 ¼ 2 Tc ¼
ð Tc ð Tc X 1 0
Rk e jkvc (st) ejvc (msnt) dt ds
0 n¼1
ð Tc ð Tc 1 1 X jvs(km) R e ds e jvt(nk) dt k T 2 n¼1 0 0
(20:11:9)
The complex exponentials {ejnvc s } and {ejnvc t } are orthogonal sets and hence 1 Tc
ð Tc e
jvs(km)
ds ¼ dkm ¼
0
1, 0,
¼ dnk ¼
k¼m
1 : T k=m c 1, n ¼ k 0,
ð Tc
e jvt(nk) ds
0
(20:11:10)
n=k
Substituting Eq. (20.11.10) into Eq. (20.11.9), we obtain the result E ½Xm Xn
1 1 X Rm , ¼ 2 Rk dkm dnk ¼ Rm dmn ¼ 0, Tc n¼1
n¼m n=k
or E ½Xm2 ¼ Rm
(20:11:11)
Thus, the Fourier coefficients of a zero mean periodic stationary random process X(t) are orthogonal and uncorrelated. If it is a Gaussian process, then these coefficients are also independent. Example 20.11.1 given by
The autocorrelation of a zero mean white Gaussian noise W(t) is
E ½W(s)W(t) ¼ RW (t s) ¼ s2 d(t s)
560
Classification of Random Processes
where s2 is the variance parameter. The process may be considered periodic with period Tc ! 1. Substituting for RW(t 2 s) in Eq. (20.11.8), we obtain s2 Tc !1 Tc2
E ½Wm Wn ¼ lim
ð Tc ð Tc 0
s2 dmn Tc !1 Tc2
d(s t)ejvc (msnt) dt ds ¼ lim
0
or s2 ¼ s2W Tc !1 Tc2
E ½Wm2 ¼ lim
and the Fourier coefficients Wm and Wn are independent for any m and n provided m = n. Cyclostationary Process
A random process X(t) is called strictly cyclostationary [21] if the joint distribution function for any set of samples at times t1,t2, . . . ,tn is the same for any integer multiples of some period T, that is, if the cdf and pdf satisfy the equations FX (x1 , . . . , xn ; t1 , . . . , tn ) ¼ FX (x1 , . . . , xn ; t1 þ kT, . . . , tn þ kT) fX (x1 , . . . , xn ; t1 , . . . , tn ) ¼ fX (x1 , . . . , xn ; t1 þ kT, . . . , tn þ kT)
(20:11:12)
for any n and k. This is somewhat restrictive, and hence we define a wide-sense cyclostationary process if the mean mX(t) and autocorrelation RX(t1,t2) are periodic: mX (t þ kT) ¼ mX (t) RX (t1 þ kT, t2 þ kT) ¼ RX (t1 , t2 )
(20:11:13)
The autocorrelation function of a wide-sense cyclostationary process is shown in Fig. 10.11.2. The difference between Figs. 20.11.2 and 20.11.1 should be noted. Example 20.11.2 A random process X(t) is defined by X(t) ¼ A cos(vc t) þ B sin(vc t), where A and B are two zero mean independent Gaussian random variables with variances
FIGURE 20.11.2
20.11 Periodic Random Process
561
s2A and s2B respectively. The mean value of this process is E ½X(t) ¼ mX (t) ¼ E ½A cos(vc t) þ E ½B sin(vc t) ¼ 0,
E ½A ¼ E ½B ¼ 0
The autocorrelation function RX(t,s) is given by RX (t, s) ¼ E ½X(t)X(s) ¼ E{½A cos(vc t) þ B sin(vc t)½A cos(vc s) þ B sin(vc s)} ¼ E ½A2 cos(vc t) cos(vc s) þ E ½B2 sin(vc t) sin(vc s) þ E ½AB½cos(vc t) sin(vc s) þ sin(vc tÞ cos(vc s) Since A and B are zero mean and independent, E ½A2 ¼ s2A , E ½B2 ¼ s2B , and E ½AB ¼ E ½AE ½B ¼ 0 and hence 1 (s t, s þ t) ¼ s2A ½cos (vc (s t)) þ cos(vc (s þ t)) 2 1 þ s2B ½cos(vc (s t)) cos(vc (s þ t)) 2 ¼
s2A þ s2B s2 s2B cos(vc (s t)) þ A cos(vc (s þ t)) 2 2
Since the autocorrelation function RX(s2t, s þ t) is dependent on both t and s, the process is not stationary. However, with mX(t) ¼ 0 and Tc ¼ 2p/vc , we have RX (s t, s þ t) ¼ RX (s t þ kTc , s þ t þ kTc ) in the preceding equation. Hence the process is wide-sense cyclostationary. Example 20.11.3
A pulse-amplitude-modulated random process is of the form 1 X
X(t) ¼
Ak p(t kTc )
k¼1
where fAkg is a stationary digital sequence of random variables with mean mA and autocorrelation function RA (h) ¼ E ½Ak Akþh . p(t) is a deterministic pulse of duration Tc. We want to find the mean and the autocorrelation function of X(t): Mean: mX (t):
1 X
E ½X(t) ¼ mX (t) ¼
1 X
E ½Ak p(t kTc ) ¼ mA
k¼1
p(t kTc )
k¼1
and the mean is a periodic function with period Tc. Autocorrelation: E ½X(t)X(s) ¼ RX (t, s) ¼
1 1 X X
E ½Ak Aj p(t kTc )p(s jTc )
k¼1 j¼1
¼
1 1 X X
RA (k, j)p(t kTc )p(s jTc )
k¼1 j¼1
¼ RX (t kTc , s kTc ), k ¼ +1, +2, . . .
562
Classification of Random Processes
FIGURE 20.11.3
Since both conditions of Eq. (20.11.13) are satisfied, we conclude that X(t) is a cyclostationary process. We will make the result more concrete by assuming that p(t) is a rectangular pulse of unit height and width Tc. The random variable sequence fAkg is an independent set of equiprobable þ1 or 21 representing the symbols 1 or 0 respectively. Since Ak is equiprobable, mA ¼ 0, and we obtain mX(t) ¼ 0. A sample waveform of X(t) corresponding to sending symbols f0100110010100g is shown in Fig. 20.11.3. Since the Ak values are independent with zero mean, we have
E ½X(t)X(s) ¼ Rx (t, s) ¼
1 X
E ½A2k p(t kTc )p(s kTc )
k¼1
1 1 With E ½A2k ¼ 1:1 þ (1) (1) ¼ 1, we obtain 2 2 2 3 kTc t , (k þ 1)Tc 1, kTc s , (k þ 1)Tc 5 RX (t, s) ¼ 4 0, otherwise The function RX (t, s) is shown in Fig. 20.11.4, which corresponds to Fig. 20.11.2, showing that X(t) is a cyclostationary process.
FIGURE 20.11.4
20.12 Aperiodic Random Process (Karhunen – Loeve Expansion)
563
B 20.12 APERIODIC RANDOM PROCESS (KARHUNEN – LOEVE EXPANSION) In the last section the coefficients of the Fourier expansion of a mean-square continuous periodic random process were found to be orthogonal random variables. We can attempt a similar Fourier expansion in the interval [a,b] for a mean-square continuous aperiodic random process X(t) as X(t) ¼
1 X
Xn fn (t),
atb
(20:12:1)
n¼1
where the sequence ffn(t)g is an orthonormal set in the interval [a,b] defined by ðb a
fn (t)fm (t)dt
¼
1, 0,
m¼n m=n
(20:12:2)
and the generalized Fourier coefficients (FCs) Xn are given by ðb Xn ¼ a
X(t)fn (t)dt,
n ¼ 0, 1, . . .
(20:12:3)
If the sequence ffn(t)g is any orthonormal set, then the coefficients fXng will not be orthogonal, unlike the periodic random processes as shown in the following example. Example 20.12.1 Let X(t), 0 t T, be a zero mean stationary Gaussian random process with continuous ACF RX(t). A Fourier series expansion of X(t) is given by X(t) ¼ lim
N!1
N X
Xn e jnv0 t :
v0 ¼
n¼N
2p T
The Fourier coefficient Xn is given by 1 Xn ¼ T
ð T=2
X(t)ejnv0 t dt
with
E ½Xn ¼ 0,
E ½X(t) ¼ 0
T=2
We will show that the random variables fXng are not orthogonal. E½Xn Xm can be written as E ½Xn Xm
1 ¼ 2 T
ð T=2 ð T=2
E½X(t)X(s)e jv0 (ntms) dt ds
T=2 T=2
Substituting m ¼ n 2 k in this equation, we obtain E ½Xn Xm
1 ¼ 2 T
ð T=2 ð T=2
RX (t s)e jv0 n(ts) ejv0 ks dt ds
T=2 T=2
Substituting t ¼ t 2 s in this equation, yields E ½Xn Xm
1 ¼ 2 T
ð T=2 ð T=2s RX (t)e T=2
T=2s
jv0 nt
dt ejv0 ks ds
(A)
564
Classification of Random Processes
We will define the quantity in Eq. (A): ð T=2s RX (t)ejv0 nt dt ¼ SX (n, s) and
lim SX (n, s) ¼ SX (nv0 )
T!1
T=2s
Substituting the preceding equation in Eq. (A), we find that ð 1 T=2 E½Xn Xm ¼ 2 SX (n, s)e jv0 ks ds = 0 T T=2 Hence we have shown that if X(t) is expanded in any orthonormal set, the resulting Fourier coefficients fXng are not orthogonal. However as T ! 1, we see that SX(n, s) tends to SX(nv0) and hence ð 1 T=2 SX (nv0 )e jv0 ks ds ! 0 lim 2 T!1 T T=2 Thus, the Fourier coefficients are orthogonal as T ! 1. We are interested in finding the necessary and sufficient conditions for that orthonormal set that will yield an orthogonal Fourier coefficient set. We first multiply Eq. (20.12.1) by Xm and take expectations E½X(t)Xm ¼
1 X
E½Xn Xm fn (t),
atb
(20:12:4)
n¼1
Under the orthogonality condition of the FCs we can substitute E½Xn Xm ¼ 0, n = m in Eq. (20.12.4) and obtain E½X(t)Xm ¼ EjXm j2 fm (t),
atb
(20:12:5)
We can obtain a second equation by multiplying the following equation for Xm ðb Xm ¼ X (s)fm (s)ds a
by X(t) and taking expectations. Or, for a t b ðb ðb E½X(t)Xm ¼ E½X(t)X (s)fm (s)ds ¼ RX (t, s)fm (s)ds
(20:12:6)
Comparing Eqs. (20.12.5) and (20.12.6), we can write ðb E½X(t)Xm ¼ RX (t, s)fm (s)ds ¼ EjXm2 jfm (t),
(20:12:7)
a
a
atb
a
Defining the signal energy EjXn2 j ¼ ln , Eq. (20.12.7) can be rewritten as ðb RX (t, s)fn (s)ds ¼ ln fn (t), a t b a
or ðb RX (t, s)f(s)ds ¼ lf(t), a
atb
(20:12:8)
20.12 Aperiodic Random Process (Karhunen – Loeve Expansion)
565
This equation which is called the Karhunen – Loeve (K-L) integral equation, which gives the necessary and sufficient conditions for the random process X(t) to be expanded as a series in terms of the orthonormal set ffm(t)g such that the generalized Fourier coefficients Xm, called Karhunen –Loeve (K-L) coefficients, are orthogonal. The expansion of Eq. (20.12.1) is called the Karhunen –Loeve (K-L) expansion. The orthonormal functions ffm(t)g are called the eigenfunctions and flmg are called the eigenvalues of the K-L integral equation. It is to be particularly noted that the process X(t) can be nonstationary. Summary: K-L equation satisfied ) Fourier coefficients{Xn } are orthogonal Fourier coefficients{Xn } are orthogonal ) K-L equation is satisfied Example 20.12.2 We are given a white-noise process with autocorrelation function RV(t, s) ¼ s2d(t 2 s). Even though this process violates the second-order condition in the sense that the autocorrelation function is not finite, we can formally find the orthonormal set such that the Fourier coefficients are orthogonal in the interval [2a, a]. Substituting for RX(t, s) in Eq. (20.12.8), we obtain ðb
s2 d(t s)fn (s)ds ¼ ln fn (t)
a
or s2 fn (t) ¼ ln fn (t) and
s2 ¼ ln ,
at a
This result conveys that any orthonormal set ffn(t)g with eigenvalues s2 will generate K-L coefficients Vn for the white-noise process in the interval [2a, a] with E[V2n] ¼ ln ¼ s2. Example 20.12.3
A random process Y(t) not necessarily stationary is given by Y(t) ¼ X(t) þ V(t)
where V(t) is a zero mean white Gaussian noise uncorrelated with X(t). We want to find the eigenfunctions ffn(t)g in the interval [2a, a] that will give K-L coefficients. The autocorrelation function of Y(t) is RY (t, s) ¼ RX (t, s) þ s2 d(t s) The eigenfunctions ffX(t)g and ffY (t)g for representing X(t) and Y(t) can be found by applying the K-L integral equation Eq. (20.12.8) to RX(t,s) and RY(t,s) as given below: ða RX (t, s)fX (s)ds ¼ lX fX (t),
a,ta
RY (t, s)fY (s)ds ¼ lY fY (t),
a,t a
a ða a
566
Classification of Random Processes
Since X(t) and V(t) are uncorrelated, we can substitute RY (t, s) ¼ RX (t, s) þ s2 d(t s) in the preceding integral equation for RY(t, s) and obtain ða ½RX (t, s) þ s2 d(t s)fY (s)ds ¼ lY fY (t), a , t a a
or ða
RX (t, s)fY (s)ds ¼ ½lY s2 fY (t),
a,t a
a
From the uniqueness of the K-L equation, fX (t) ¼ fY (t) and lY ¼ lX þ s2 . Hence, we can use the same eigenfunction set ffX (t)g for the series expansion of both X(t) and Y(t), and from Example 20.12.1 the same can be used for expanding V(t). Thus, all three signals X(t), Y(t), and V(t) can be represented for 2a , t a in the same function space as X(t) ¼
1 X
Xn fn (t),
Y(t) ¼
n¼1
1 X
Yn fn (t),
V(t) ¼
n¼1
1 X
Vn fn (t)
n¼1
The previous two examples are simple ones. However, solving the general K-L integral equation can be difficult. In particular cases, closed-form solutions can be obtained as shown in the following examples. Example 20.12.4 (Nonstationary Process) Wiener process W(t) is
The autocorrelation function RW(t, s) of the
RW (t, s) ¼ s2 min(t, s)
(20:12:9)
The series expansion for W(t) for the time of observation [0, T ] can be obtained from the K-L integral equation [Eq. (20.12.8)] as follows: ðT s2 min(t, s)f(s)ds ¼ lf(t) 0
or s2
ð t
ðT sf(s)ds þ t
0
f(s)ds ¼ lf(t),
0 , t, s T
(20:12:10)
t
The usual procedure to solve integral equations of this type is to differentiate them as many times as possible so that they are converted into differential equations. The resulting differential equations are then solved from the boundary conditions obtained from their form. Differentiating Eq. (20.12.10) with respect to t, there results ðT 2 s tf(t) þ f(s)ds tf(t) ¼ lf(t) t
or s2
ðT f(s)ds ¼ lf(t),
0 , t, s T
(20:12:11)
t
Differentiating again with respect to t [Eq. (20.12.11)], we obtain a second-order homogeneous differential equation given by f(t) þ
s2 ¼ 0, l
0 , t T, l = 0
(20:12:12)
20.12 Aperiodic Random Process (Karhunen – Loeve Expansion)
567
pffiffiffi The characteristic equation is s 2 þ (s2/l) ¼ 0, and the roots are s1 , s2 ¼ +(s= l). The solution to the differential equation is st st f(t) ¼ A cos pffiffiffi þ B sin pffiffiffi (20:12:13) l l There are three unknowns—A, B, and l—to be resolved. The boundary conditions are obtained as follows. Substituting t ¼ 0 þ in Eq. (20.12.10), we can write Ð 0þ s2 0 min (t, s)f(s)ds ¼ lf(0 þ), or lf(0 þ) ¼ 0 and f(0 þ) ¼ 0. Substituting this initial condition in Eq. (20.12.13), we have A ¼ 0 and the solution becomes st f(t) ¼ B sin pffiffiffi (20:12:14) l ÐT Substituting t ¼ T2 in Eq. (20.12.11), we obtain s2 T f(s)ds ¼ lf(T ), or pffiffiffi pffiffiffi f(T ) ¼ 0. Differentiating Eq. (20.12.14), f(t) ¼ B cos (st= l)s= l and we have 2 sT sT B cos pffiffiffi ¼ 0 from which ln ¼ ,n 1 (20:12:15) (2n 1)p=2 l The constant B is obtained from the normalization integral given by ðT B2 sin2 (kt)dt ¼ 1
(20:12:16)
0
where, for convenience, we have defined s s(2n 1)p (2n 1)p ¼ k ¼ pffiffiffiffiffi ¼ 2sT 2T ln The integral in Eq. (20.12.16) yields ðT ðT 2 B B2 T sin(2kT) 2 2 ¼1 ½1 cos(2kt)dt ¼ B sin (kt)dt ¼ 2 2k 0 0 2
(20:12:17)
Withpkffiffiffiffiffiffiffiffi ¼ (2n 1)p=2T, the term sin (2kT)=2k in Eq. (20.12.17) goes to zero and B ¼ 2=T . Hence the eigenfunctions fn(t) are rffiffiffiffi 2 (2n 1)pt n1 fn (t) ¼ sin , (20:12:18) 0,tT T 2T The K-L expansion of the Wiener process W(t) is given by rffiffiffiffi 1 X 2 (2n 1)pt W(t) ¼ Wn sin , 0,tT T 2T n¼1 where the coefficients Wn are given by an equation similar to Eq. (20.12.3) rffiffiffiffi ðT 2 (2n 1)pt n1 Wn ¼ W(t) sin dt, 0,tT T 2T 0
(20:12:19)
(20:12:20)
with the property E ½Wn2 ¼ ln , n 1. Example 20.12.5 A sinusoid with random phase is given by X(t) ¼ A cos(vt þ F), where A and v are constants and F is uniformly distributed in (2p, p). The
568
Classification of Random Processes
autocorrelation function as derived in Example 19.2.1 is RX(t, s) ¼ (A 2/2) cos [v(t 2 s)]. We have to find the K-L expansion for X(t) in the period [2T/2, T/2] and show that the K-L coefficients Xn are orthogonal. Substituting for the autocorrelation function in the K-L equation [Eq. (20.12.8)], we obtain ð A2 T=2 T T cos½v(t s)f(s)ds ¼ lf(t), t, s 2 2 2 T=2 Taking derivative with respect to t once, we get ð T=2 A2 v sin½v(t s)f(s)ds ¼ lf(t), 2 T=2
T T t, s 2 2
Taking the derivative with respect to t again, we have ð A2 2 T=2 v cos½v(t s)f(s)ds ¼ lf(t) 2 T=2 or v2 lf(t) ¼ lf(t) or
f(t) þ v2 f(t) ¼ 0,
T T t, s 2 2
The solution to the second-order homogeneous differential equation above is given by f(t) ¼ C cos(vt) þ D sin(vt) From the form of f(t) we conclude that the eigenfunctions are rffiffiffiffi rffiffiffiffi 2 2 f1 (t) ¼ cos(vt) f2 (t) ¼ sin(vt) T T There are only two terms in the finite K-L expansion given by rffiffiffiffi rffiffiffiffi 2 2 X(t) ¼ X1 cos(vt) þ X2 cos(vt) T T We can find these two K-L coefficients by equating like coefficients in the expansion of X(t) given by X(t) ¼ A cos(vt) cos(F) A sin(vt) sin(F) Thus rffiffiffiffi T cos(F) X1 ¼ A 2
rffiffiffiffi T and X2 ¼ A sin(F) 2
With F uniformly distributed in (2p, p), the joint moment E[X1X2] is E ½X1 X2 ¼
A2 T A2 T E ½cos (F) sin (F) ¼ E ½sin (2F) ¼ 0 2 2
showing that the K-L coefficients are orthogonal. We will now find the eigenvalue l1 by substituting f1(t) in the K-L equation: rffiffiffiffi rffiffiffiffi ð A2 T=2 2 2 cos½v(t s) cos(vs)ds ¼ cos(vt) T T 2 T=2
20.12 Aperiodic Random Process (Karhunen – Loeve Expansion)
569
or A2 2
ð T=2
1 {cos(vt) þ cos½v(t 2s)}ds ¼ l1 cos(vt) T=2 2
or A2 T cos(vt) ¼ l cos(vt) and 4
l1 ¼
A2 T 4
In a similar manner, substituting f2(t) in the K-L equation, l2 ¼ A 2T/4. The energy values in the K-L coefficients are E ½X12 ¼
A2 T A2 T E ½cos2 (F) ¼ ¼ l1 2 4
E ½X12 ¼
A2 T A2 T E ½sin2 (F) ¼ ¼ l2 2 4
Example 20.12.6 The autocorrelation function of a random process X(t) is RX (t, s) ¼ e2jtsj . The signal is observed in the interval [22, 2]. We want to find the eigenvalues and eigenfunctions. The function RX (t, s) ¼ e2jtsj is shown in Fig. 20.12.1. This example a little more involved than the previous ones, and since X(t) is not specified, we cannot obtain the K-L coefficients directly. The K-L equation is obtained by substituting RX(t,s) in Eq. (20.12.8). The result is ðt
e2(ts) f (s)ds þ s,t
2
ð2 t
e2(ts) f (s)ds ¼ lf(t), 2 t 2 st
Differentiating this equation with respect to t, we obtain ðt e
2 2
2(ts)
ð2 f(s)ds þ 2
e2(ts) f(s)ds ¼ lf(t)
t
FIGURE 20.12.1
570
Classification of Random Processes
Differentiating again yields ð t ð2 4f(t) þ 4 e2(ts) f(s)ds þ e2(ts) f(s)ds ¼ lf(t) t
2
The quantity within braces is the K-L equation and equals lf(t). Hence we obtain lf(t) þ 4(1 l)f(t) ¼ 0,
2t 2
a second-order homogeneous differential equation. The characteristic equation is s2 þ 4(1 l)=l ¼ 0, and the oscillatory roots are given by v2 ¼ 4(1 l)=l. We have an implicit relationship between v and l with l ¼ 4=(4 þ v2 ). The two eigenfunctions are of the form f1 (t) ¼ A cos(vt) and f2 (t) ¼ B sin(vt), where the constants A and B are obtained from normalization. Substituting f1(t) in the K-L equation, we obtain ðt ð2 e2t e2s cos(vs)ds þ e2t e2s cos(vs)ds ¼ l cos(vt) ðBÞ 2 t ðt 1 e2t e2s cos(vs)ds ¼ {2 cos(vt) þ v sin(vt) e4 e2t ½2 cos(2v) þ v sin(2v)} 4 þ v2 2 ð2 1 {2 cos(vt) v sin(vt) e4 e2t ½2 cos(2v) þ v sin(2v)} e2t e2s cos(vs)ds ¼ 4 þ v2 t Substituting these equations in Eq. (B) we obtain 1 {4 cos(vt) þ e4 (e2t þ e2t )½v sin(2v) 2 cos(2v)} ¼ l cos(vt) 4 þ v2 Equating like coefficients on both sides of this equation, yields l¼
4 4 þ v2
and
v sin(2v) 2 cos(2v) ¼ 0
The first equation has been obtained earlier. The second equation can be simplified as tan(2v) ¼ 2/v. This is a transcendental equation that has to be solved graphically for v. It is simpler to solve graphically g(v) ¼ (v=2) tan(2v) ¼ 1 for v as shown in Fig. 20.12.2.
FIGURE 20.12.2
20.12 Aperiodic Random Process (Karhunen – Loeve Expansion)
571
The solution to g(v) ¼ 1 is v ¼ 0.6323. Having found v, we can find A and B from ð2 sin(4v) 2 2 2 A cos (vt)dt ¼ 1; or A 2 þ ¼ 1, hence A ¼ 0:6383 2v 2 ð2 sin(4v) 2 2 2 B sin (vt)dt ¼ 1, or B 2 ¼ 1, hence B ¼ 0:8044 2v 2 The eigenvalue is l ¼ 4=(4 þ v2 ) ¼ 0:9091. The eigenfunctions are f1 (t) ¼ 0:6383 cos(0:6323t) and f2 (t) ¼ 0:8044 sin(0:6323t). The K-L expansion is X(t) ¼ X1 f1 (t) þ X2 f2 (t), 2 t 2 and E[X21] ¼ E[X22] ¼ l ¼ 0.9091. Discrete Karhunen– Loeve Transform
In the last section we discussed the continuous Karhunen – Loeve expansion. The same type of an expansion is applicable to a discrete aperiodic random sequence fX (n)g. We shall assume that fX (n)g is a second-order random sequence with autocorrelation RX(n, m). Similar to the continuous-time case, the Fourier expansion for X(n) can be given by X(n) ¼
N 1 X
Xi fi (n),
n ¼ 0, . . . , N 1
(20:12:21)
i¼0
where Xi is the discrete generalized Fourier transform and ffi (n)g is an orthonormal set in the interval 0 n N 2 1. We want to find the discrete equivalent of the integral equation (20.12.8) satisfied by the orthonormal sequence ffi(n)g. The generalized Fourier coefficients Xi are given by Xi ¼
N 1 X
X(n)fi (n),
0iN1
(20:12:22)
n¼0
If the sequence ffi(n)g is any orthonormal set, then the coefficients fXig in general will not be orthogonal. We are interested in finding the necessary and sufficient conditions for that orthonormal set ffi(n)g that will yield an orthogonal set of Fourier coefficient fXig. Multiplying Eq. (20.12.21) by Xk and taking expectations, we can write E ½X(n)Xk ¼
N 1 X
E ½Xi Xk fi (n),
0nN1
(20:12:23)
i¼0
Substituting the desired orthogonality criterion E ½Xi Xk ¼ 0, i = k of the Fourier coefficients in Eq. (20.12.23) yields E ½X(n)Xk ¼ EjXk j2 fk (n),
0 n, k N 1
(20:12:24)
We can obtain a second equation by multiplying the equation for X k Xk ¼
N 1 X
X (m)fk (m),
0k N 1
m¼0
by X(n) and taking expectations. Thus, for 0 n, k N 2 1, we obtain E ½X(n)Xk ¼
N 1 X m¼0
E ½X(n)X (m)fk (m) ¼
N 1 X m¼0
RX (n, m)fk (m)
(20:12:25)
572
Classification of Random Processes
Comparing Eqs. (20.12.24) and (20.12.25), we can write E ½X(n)Xk ¼
N1 X
RX (n, m)fk (m) ¼ EjXk j2 fk (n),
0nN1
(20:12:26)
m¼0
As in the continuous case, we can define the signal energy EjXk j2 ¼ lk and rewrite Eq. (20.12.26) as N 1 X
RX (n, m)fk (m) ¼ lk fk (n),
0nN 1
(20:12:27a)
m¼0
or N 1 X
RX (n, m)f(m) ¼ lf(n),
0nN1
(20:12:27b)
m¼0
Equation (20.12.27) is a necessary and sufficient condition for the existence of an orthonormal set that will give an orthogonal set of coefficients fXkg in the Fourier series expansion for random sequence X(n). The convergence of Eq. (20.12.21) is in the mean-square sense, or 2 N 1 X E X(n) Xk fk (n) ¼ 0, k¼0
n ¼ 0, . . . , N 1
(20:12:28)
Matrix Formulation of K-L Equation
The K-L equation [Eq. (20.12.27a)] can be compactly expressed in matrix form. The complex random sequence fX(n)g can be expressed as an N-vector X as X ¼ X(0)
X(1)
X(N 1)
T
(20:12:29)
with the autocorrelation matrix RX ¼ E[XX H] given by 2
RX (0, 0) 6 RX (1, 0) 6 RX ¼ 6 .. 4 . RX (N 1, 0)
3 RX (0, 1) RX (0, N 1) 7 RX (1, 1) RX (1, N 1) 7 7 .. .. 5 . . RX (N 1, 1) RX (N 1, N 1)
(20:12:30)
where the superscript H stands for conjugate transpose. We have to find the condition under which F an N N transformation matrix given by 2 3 f01 f0N1 f00 f f11 f1N1 7 6 6 10 7 F ¼ f0 f 1 fN1 ¼ 6 . (20:12:31) 7 .. . .. 4 .. 5 . fN10 fN11 FN1N1
20.12 Aperiodic Random Process (Karhunen – Loeve Expansion)
573
transforms the random sequence vector X in Eq. (12.20.29) into an orthogonal random sequence vector X given by T (20:12:32) X ¼ FH X ¼ X0 X1 XN1 with E ½XXH ¼ L, a diagonal matrix with components flkg. In this case the transformation matrix F has to satisfy the matrix K-L equation given by RX F ¼ LF
(20:12:33)
This equation is similar to the matrix eigenequation (16.2.5). Hence the transforming matrix F is the modal matrix of RX, and since RX is symmetric and positive definite, F is unitary, that is, F 21 ¼ F H. The eigenvalues of RX constitute the diagonal matrix L with elements lk ¼ E[X2k ], n ¼ 0, 1, . . . , N 2 1.
CHAPTER
21
Random Processes and Linear Systems
B 21.1
REVIEW OF LINEAR SYSTEMS
Consider the deterministic linear time-invariant system shown in Fig. 21.1.1 with input x(t) and output y(t). The system function is characterized in the time domain as the output to a Dirac delta function d(t) with x(0) ¼ 0 and is called the impulse response h(t). A causal system is one that does not anticipate the input meaning that the impulse response h(t) ¼ 0, t , 0. The output y(t) can be given as a convolution of the input x(t) and the impulse response h(t). Depending on the causality of x(t) and h(t), the solution y(t) in continuous time is given by a convolution integral and may contain an initial condition term x(0) as shown below: Ðt Ðt 2 x(0)h(t) þ 0 x(t)h(t t)dt ¼ x(0)h(t) þ 0 x(t t)h(t)dt, x(t), h(t) causal Ð1 6Ðt y(t) ¼ 4 1 x(t)h(t t)dt ¼ 0 x(t t)h(t)dt, x(t) noncausal, h(t) causal Ð1 Ð1 x(t), h(t) noncausal 1 x(t)h(t t)dt ¼ 1 x(t t)h(t)dt, (21:1:1) and similar equations in discrete time for the solution y(n) with initial condition x(0) 2 n P x(0)h(n) þ x(m)h(n m) 6 m¼1 6 6 n 6 ¼ P x(n m)h(m), x(n), h(n) causal 6 6 m¼0 6 P 6 n 6 x(m)h(n m) 6m¼1 (21:1:2) y(n) ¼ 6 1 6 6 ¼ P x(n m)h(m), x(n) noncausal, h(n) causal 6 m¼0 6 6 P 6 1 x(m)h(n m) 6 6m¼1 6 1 4 P ¼ x(n m)h(m), x(n), h(n) noncausal m¼1
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
574
21.1
Review of Linear Systems
575
FIGURE 21.1.1
The Fourier transform (FT) of the impulse response h(t) is called the transfer function H(v) and is shown below: ð1 h(t)ejvt dt (21:1:3) H(v) ¼ 0
The input FT X(v) and the output FT Y(v) are related by Y(v) ¼ H(v)X(v)
(21:1:4)
The time-invariant system can also be characterized in continuous time by state equations of the form d x(t) ¼ Ax(t) þ Bu(t), x(0) d
(21:1:5)
y(t) ¼ Cx(t) and in discrete time by x(n þ 1) ¼ Ax(n) þ Bu(n), x(0) y(n) ¼ Cx(n)
(21:1:6)
In Eqs. (21.1.5) and (21.1.6) x is an n-vector, A is an n n matrix, B is an n m matrix, u is an m-vector, y is a p-vector, C is a p n matrix, and D is a p m matrix. If these equations are time-varying, then the matrices A, B, and C will become A(t), B(t), and C(t). The corresponding matrix solutions for y for a causal system with initial condition x(0) are Continuous Time: ðt F(t t)Bu(t)dt, time-invariant
y(t) ¼ CF(t)x(0) þ 0
(21:1:7)
ðt F(t, t)B(t)u(t)dt, time-varying
y(t) ¼ C(t)F(t, 0)x(0) þ 0
Discrete Time: y(n) ¼ CF(n)x(0) þ
n X
F(n m)Bu(m), time-invariant
m¼0
y(n) ¼ C(n)F(n, 0)x(0) þ
n X
(21:1:8) F(n, m)B(m)u(m), time-varying
m¼0
In Eqs. (21.1.7) and (21.1.8) the term F (.) is called the state transition matrix. The time-invariant system in Eq. (21.1.5) can be solved in the frequency domain by Laplace transform (LT) techniques. Taking unilateral LT of the first equation in
576
Random Processes and Linear Systems
Eq. (21.1.5) and using the initial condition x(0), we obtain sX(s) x(0) ¼ AX(s) þ BU(s) or (sI A)X(s) ¼ x(0) þ BU(s) Hence X(s) ¼ (sI A)1 x(0) þ (sI A)1 BU(s)
(21:1:9)
The inverse LT of the matrix (sI 2 A)21 term by term yields the state transition matrix F(t). The solution for Y(s) is given by substituting Eq. (21.1.9) in the LT of the second equation in Eq. (21.1.5): Y(s) ¼ C(sI A)1 x(0) þ C(sI A)1 BU(s)
(21:1:10)
In general, deterministic systems deal with causal input x(t) and causal impulse response h(t), and the resulting output y(t) will also be causal. However, for random inputs to a system, x(t) need not be causal whereas h(t) will be causal. The resulting output y(t) also will not be causal, as shown in the following example. Example 21.1.1 In the system represented by Fig. 21.1.1, x(t), is a noncausal signal given by x(t) ¼ e 22jtj and the impulse response is causal, given by h(t) ¼ et u(t). We will find the output using Laplace transforms (LTs). The LTs of x(t) and h(t) are given by X(s) ¼
4 4 s2
and
H(s) ¼
1 sþ1
The LT Y(s) of the output y(t) is given by Y(s) ¼ H(s)X(s) ¼
4 1 4 s2 s þ 1
The partial fraction expansion of Y(s) yields Y(S) ¼
4 1 4 1 1 ¼ þ 2 4 s s þ 1 3(s þ 1) s þ 2 3(2 s)
and the inverse LT gives the output as 4 1 y(t) ¼ et u(t) e2t u(t) þ e2t u(t) 3 3 x(t), h(t), and y(t) are shown in Fig. 21.1.2. The results can also be obtained in the time domain directly from the convolution integral ð1 x(t t)h(t)dt y(t) ¼ 0
and h(t) and x(t 2 t) are shown in Fig. 21.1.3. From the figure we can evaluate the convolution. Since x(t 2 t) is given by 2(tt) e , tt x(t t) ¼ e2jttj ¼ e2(tt) , t . t
21.1
Review of Linear Systems
577
FIGURE 21.1.2
we can calculate y(t) in the two regions t 0 and t . 0. For t 0, we have ð1 y(t) ¼
e2(tt) et dt ¼ e2t
0
ð1 0
1 e3t dt ¼ e2t , 3
t 0, t . t
The case for t . 0 is shown in Fig. 21.1.3. We calculate the convolution integral in the two regions t t and t . t as shown below: ðt e
y(t) ¼ 0
2(tt) t
e
tt
ð1 dt þ
e t
2(tt)
t
e dt ¼ e
2t
ðt
t
e dt þ e
2t
ð1
0
t.t
1 ¼ et e2t þ et , 3 The final result for y(t) is as obtained by the transform method.
FIGURE 21.1.3
e3t dt
t
t.0
578
Random Processes and Linear Systems
B 21.2 RANDOM PROCESSES THROUGH LINEAR SYSTEMS The input to the system of Fig. 21.1.1 is a random process fX(t), 21 , t , 1g. The system function h(t) is causal. The sample output Y(t, j) of the system for each sample function X(t, j) is given by ð1 Y(t, j) ¼ X(t t, j)h(t)dt (21:2:1) 0
This is a more convenient form for a noncausal X(t) and causal h(t) than the form ðt X(t, j)h(t t)dt (21:2:2) Y(t, j) ¼ 1
Under the following mild conditions on h(t) ð1 ð1 jh(t)j2 dt , 1 and h(t t)CX (t, s)h(t s)dt , 1 0
(21:2:3)
0
the output fY(t), 21 , t , 1g is also a random process. We can now obtain the statistics of the random process Y(t) under the condition that the input random process X(t) is stationary. (It has already been mentioned that stationary means wide-sense stationary.) The mean value of Y(t) is obtained by taking expectations of both sides of the equation ð1 Y(t) ¼ X(t t)h(t)dt 0
or ð1
ð1 E½Y(t) ¼ mY (t) ¼
E½X(t t)h(t)dt ¼ mX 0
and on substituting H(0) ¼
Ð1 0
h(t)dt ¼ mY
(21:2:4)
0
h(t)dt from Eq. (21.1.3), we obtain mY ¼ mX H(0)
(21:2:5)
Thus the mean value of Y(t) is a constant. The first step in obtaining the autocorrelation function RY (t, t) of the output Y(t) is to premultiply Eq. (21.2.4) by X(t – t) and take expectations. Thus ð1 E½X(t t)X(t j)h(j)dj E½X(t t)Y(t) ¼ 0
or ð1 RX (t j)h(j)dj
RXY (t) ¼ 0
or RXY (t) ¼ RX (t) h(t)
(21:2:6)
Thus the cross-correlation function RXY(t) between the input X(t) and the output Y(t) is the convolution between the input autocorrelation function RX(t) and the impulse
21.2
Random Processes through Linear Systems
579
FIGURE 21.2.1
response h(t). The second step is to postmultiply Eq. (21.2.4) by Y(t þ t) and take expectations. Thus ð1 E½Y(t)Y(t þ t) ¼ E½X(t j)Y(t þ t)h(j)dj 0
or ð1 RXY (t þ j)h(j)dj
RY (t) ¼ 0
or RY (t) ¼ RXY (t) h(t)
(21:2:7)
Combining Eqs. (21.2.6) and (21.2.7), we obtain RY (t) ¼ RX (t) h(t) h(t)
(21:2:8)
In many cases it is easier to solve Eq. (21.2.8) in the frequency domain. Taking the Fourier transform of Eq. (21.2.8), the power spectral densities of the input and the output are related by SXY (v) ¼ SX (v)H(v) SY (v) ¼ SXY (v)H (v)
(21:2:9)
SY (v) ¼ SX (v)jH(v)j2 The schematic of the input –output transformation is shown in Fig. 21.2.1. In conclusion, we can state that if a stationary random process X(t) is passed through a linear system whose impulse response h(t) satisfies the regularity conditions of Eq. (21.2.3), then the output Y(t) is also a stationary process. In most problems involving random processes through linear systems, the output autocorrelation RY(t) can be more easily found by first finding the psd SY(v) from Eq. (21.1.9) and taking the inverse Fourier transform. Example 21.2.1 A white-noise process X(t) with autocorrelation function RX(t) ¼ s2d(t) is passed through a linear system whose impulse response h(t) is given by h(t) ¼ e 2atu(t). We want to find the mean value mY of the output, the cross-correlation function RXY(t), the output autocorrelation function RY (t), and the variance of the output s2Y. The transfer function H(v) and its absolute value squared jH(v)j2 are given by H(v) ¼
1 1 , jH(v)j2 ¼ 2 a þ jv a þ v2
and the input power spectral density is given by SX(v) ¼ s2. The mean value mY is m mY ¼ mX H(0) ¼ X ¼ 0 since mX ¼ 0 a
580
Random Processes and Linear Systems
The cross-spectral density SXY(v) and the output psd SY(v) from Eq. (21.2.9) are SXY (v) ¼ s2 H(v) ¼
s2 ; a þ jv
SY (v) ¼ SX (v)jH(v)j2 ¼
a2
s2 þ v2
Taking inverse Fourier transforms of SXY(v) and SY(v), we obtain the cross-correlation function RXY(t) and the autocorrelation function RY(t): RXY (t) ¼ s2 eat u(t);
RY (t) ¼
s2 ajtj e 2a
Since the mean mY ¼ 0, the variance of the output is s2Y ¼ RY (0) ¼ E½Y 2 ¼ s2 =2a. This filter is called a shaping filter since it shapes a white noise to any desired psd by means of the factor a. Example 21.2.2 The autocorrelation function RX(t) of the input random process X(t) in the previous example is modified as RX(t) ¼ a 2 þ s2d(t) with the impulse response h(t) ¼ e – atu(t). The mean value of X(t) from Eq. (19.2.26) is mX ¼ a. Hence the mean value of Y(t) is m a mY ¼ mX H(0) ¼ X ¼ a a The input psd is SX(v) ¼ a 22pd(v) þ s2. The cross-spectral density SXY(v) and the output psd SY(v) from Eq. (21.2.9) are SXY (v) ¼ s2 þ 2pa2 d(v) H(v) ¼ SY (v) ¼
a2
s2 2pa2 þ d(v) a þ jv a
s2 a2 þ 2 2pd(v) 2 þv a
The corresponding cross-correlation and autocorrelation functions of Y(t) are RXY (t) ¼ s2 eat u(t) þ
a2 ; a
RY (t)¼
s2 ajtj a2 e þ 2 2a a
Example 21.2.3 In this example the input X(t) is a random telegraph wave as in Example 19.2.7 with h(t) ¼ e – atu(t) and autocorrelation function RX (t) ¼ 14 {1 þ e2ljtj }. We have to find the statistics of the output process Y(t). The mean value, the autocovariance, and the variance X(t) are rffiffiffi 1 1 1 1 CX (t) ¼ e2ljtj mX ¼ s2X ¼ CX (0) ¼ ¼ 4 2 4 4 The input psd is SX (v) ¼
1 4l 2pd(v) þ 2 4 4l þ v2
The mean value of the output is mY ¼ mX H(0) ¼
mX 1 ¼ 2a a
21.2
Random Processes through Linear Systems
581
The cross-spectral density SXY(v) and the output psd SY(v) from Eq. (21.2.9) are 1 4l 1 2pd(v) l 2pd(v) þ 2 ¼ þ SXY (v) ¼ 4 4a 4l þ v2 a þ jv (4l2 þ v2 )(a þ jv) 1 4l 1 2pd(v) l 2pd(v) þ 2 SY (v) ¼ ¼ þ 2 2 2 2 2 2 4 4a 4l þ v a þ v (4l þ v )(a2 þ v2 ) The partial fraction expansions of SXY(v) and SY(v) are 1 1 l 2pd(v) 4(a 2l) 4(a þ 2l) a2 4l2 þ þ SXY (v) ¼ 2l þ jv 2l jv 4a a þ jv SY (v) ¼
2pd(v) 1 4l l 2a þ 4a2 4(a2 4l2 ) (4l2 þ v2 ) 2a(a2 4l2 ) (a2 þ v2 )
The corresponding cross-correlation and autocorrelation functions of Y(t) are 2 2lt 1 e 1 6 4 a þ 2l þ a , t 0 6 RXY (t) ¼ 6 2lt 41 e 1 leat þ , t .0 2 4 a 2l a a 4l2 1 e2ljtj 1 leajtj RY (t) ¼ þ 4 a2 4l2 a2 2a(a2 4l2 ) The cross-spectral density SXY (v), the cross-correlation function RXY (t), the power spectral density SY(v), and the autocorrelation function RY (t) are shown for l¼1 and a ¼ 0.5 in Figs. 21.2.2 –21.2.5. The variance of the output process Y(t) is given by s2Y ¼ RY (0) m2Y ¼
4(a2
1 l ¼ 0:2 2 2 4l ) 2a(a 4l2 )
FIGURE 21.2.2
582
Random Processes and Linear Systems
FIGURE 21.2.3
FIGURE 21.2.4
FIGURE 21.2.5
21.2
Random Processes through Linear Systems
583
Example 21.2.4 The input to a linear system is a zero mean stationary random process X(t), and the output is Y(t). Their psds are given by SX (v) ¼
1 4 : ; SY (v) ¼ 4 þ v2 (4 þ v2 )(64 þ v2 )
The autocorrelation of the input and the output are given by their respective inverse Fourier transforms (IFT): 1 4 e2jtj RX (t) ¼ IFT ¼ 4 ð4 þ v2 Þ 4 4 1 4 1 16 RY (t) ¼ IFT ¼ IFT (4 þ v2 )(64 þ v2 ) 60 (4 þ v2 ) 240 (64 þ v2 ) ¼
1 2jtj 4e e8jtj 240
Since the mean mX ¼ 0, the mean mY ¼ 0. The variances are 1 s2X ¼ , 4
s2Y ¼
1 1 1 ¼ 60 240 80
Transfer function H(v) and the impulse response h(t) can be found as follows. From Eq. (21.2.9) we can write jH(v)j2 ¼
SY (v) 4 2 2 ¼ ¼ H(v)H(v) ¼ 2 SX (v) 64 þ v (8 þ jv) (8 jv)
Hence the transfer function and the impulse responses are given by H(v) ¼
2 ; h(t) ¼ 2e8t u(t) (8 þ jv)
The cross-spectral density is given by SXY (v) ¼
1 2 (4 þ v2 ) (8 þ jv)
The partial fraction expansion of SXY(v) yields SXY (v) ¼
1 1 1 þ 12(2 þ jv) 20(2 jv) 30(8 þ jv)
and the cross-correlation function RXY(t) is given by the IFT of SXY(v): RXY (t) ¼
1 2t 1 1 e u(t) þ e2t u(t) e8t u(t) 20 12 30
The Cross-correlation function RXY(t) is shown in Fig. 21.2.6. Example 21.2.5 A random process X(t) with autocorrelation function RX (t) ¼ 1 þ 4e2jtj excites a linear system with impulse response h(t). The autocorrelation function of the output Y(t) is given by RY (t) ¼ 1 þ 83 ejtj 43 e2jtj . We will find the various statistics of this system. Since RX(1) ¼ 1, the mean value mX ¼ 1. The variance s2X ¼ 4.
584
Random Processes and Linear Systems
FIGURE 21.2.6
The power spectral densities of X(t) and Y(t) are the FTs of the corresponding autocorrelations: 16 4 þ v2 16 16 SY (v) ¼ 2pd(v) þ 3(1 þ v2 ) 3(4 þ v2 )
SX (v) ¼ 2pd(v) þ
¼ 2pd(v) þ
16 (1 þ
v2 )(4
þ v2 )
The presence of the delta function complicates the analysis somewhat. If H(v) is the required transfer function, then from Eq. (21.2.9) we have 16 16 2pd(v) þ jH(v)j2 ¼ 2pd(v) þ 4 þ v2 (1 þ v2 )(4 þ v2 ) 2pd(v)jH(0)j2 þ
16 16 jH(v)j2 ¼ 2pd(v) þ 2 2 4þv (1 þ v )(4 þ v2 )
Since the singularity term on the lefthand side can be matched only by the singularity term on the righthand side, we have jH(0)j2¼1 and 16 16 jH(v)j2 ¼ 2 2 4þv (1 þ v )(4 þ v2 ) from which jH(v)j2 ¼
1 1 1 ¼ (1 þ v2 ) 1 þ jv 1 jv
Hence, the transfer function H(v) ¼ 1=(1 þ jv) and the impulse response h(t) ¼ et u(t). The mean value of the output mY ¼ mX H(0) ¼ 1. The variance of the output is 8 4 4 s2Y ¼ RY (0) m2Y ¼ ¼ 3 3 3
21.2
Random Processes through Linear Systems
585
FIGURE 21.2.7
The cross-spectral density SXY(v) is given by SXY (v) ¼ 2pd(v) þ
16 4 þ v2
1 16 1 ¼ 2pd(v) þ 1 þ jv 4 þ v2 1 þ jv
and the cross-correlation function RXY(t) by 4 16 RXY (t) ¼ 1 þ e2t u(t) 4e2t u(t) þ et u(t) 3 3 which is shown in Fig. 21.2.7. Example 21.2.6 A zero mean stationary Gaussian stochastic process X(t) with autocorrelation function RX(t) ¼ 100e 2100jtj is applied to a system shown in Fig. 21.2.8. From the diagram the output Y(t) is another random process given by Y(t) ¼ X(t)– X(t – 0.1p). We will determine the various statistics of the random processes. Since mX ¼ 0, it follows that RX(t) ¼ CX(t) and hence the variance of X(t), s2X ¼ CX (0) ¼ 100. The joint density of fX1 ¼ X(t – 0.1p), X2 ¼ X(t)g can be found by first obtaining the correlation coefficient rX1X2: E½X1 X2 ¼ E½X(t 0:1p)X(t) ¼ RX (0:1p) ¼ CX (0:1p) ¼ 100e10p
FIGURE 21.2.8
586
Random Processes and Linear Systems
Hence rX1 X2 ¼
CX (0:1p) ¼ e10p CX (0)
The joint Gaussian density between X(t1) and X(t2) can be obtained from Eq. (9.3.1) as follows: 2 3 1 6 2(1 r2 ) 7 1 X1 X2 6 7 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp6 2 fX1 X2 (x1 , x2 ) ¼ 2 7 x 2r x x þ x 4 2 2 X1 X2 1 2 2 5 2psX 1 rX1 X2 1 2 sX 2 3 1 6 2(1 e20p ) 7 1 2 7 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp6 ¼ 10p 2 5 4 20p x1 2e x1 x2 þ x2 200p 1 e 100 The power spectral density SX(v) of the input random process X(t) is SX (v) ¼
20,000 10,000 þ v2
The transfer function of the system is H(v) ¼
Y(v) (1 ej0:1pv )X(v) ¼ ¼ 1 ej0:1pv X(v) X(v)
Hence the mean value mY ¼ mXH(0) ¼ 0. The cross-spectral density SXY (v) and the power spectral density SY (v) are obtained from Eqs. (21.2.9) as follows: SXY (v) ¼ SX (v)H(v) ¼
20,000 (1 ej0:1pv ) 10,000 þ v2
SY (v) ¼ SX (v)H(v)H(v) ¼
20,000 (1 ej0:1pv )(1 e j0:1pv ) 10,000 þ v2
¼
20,000 (2 ej0:1pv e j0:1pv ) 10,000 þ v2
¼
40,000½1 cos (0:1pv) 10,000 þ v2
The graphs of jSXY (v)j and SY (v) are shown in Figs. 21.2.9 and 21.2.10. Taking the IFTs of SXY (v) and SY (v) from the FT tables yields the correlation functions RXY (t) and RY (t), given by
RXY (t) ¼ IFT½SXY (v) ¼ 100 e100jtj e100jt0:1pj
RY (t) ¼ FT½SY (v) ¼ 100 2e100jtj e100jt0:1pj e100jtþ0:1pj The graphs of RXY(t), and RY(t) are shown in Figs. 21.2.11 and 21.2.12. The variance of the process Y(t) is given by s2Y ¼ CY (0) ¼ RY (0) ¼ 200. With 2 sY ¼ 200, the pdf of Y(t) is given by fY (y) ¼
1 2 pffiffiffiffi e(1=2){y =200} 20 p
21.2
Random Processes through Linear Systems
FIGURE 21.2.9
FIGURE 21.2.10
FIGURE 21.2.11
587
588
Random Processes and Linear Systems
FIGURE 21.2.12
FIGURE 21.2.13
from which the probability of Y(t) . y can be calculated from ðy 2 1 e(1=2){z =200} dz P{Y(t) . y} ¼ 1 FY (y) ¼ 1 pffiffiffiffi 20 p 1 and P(Y(t) . 10) can be evaluated. The function 1 2 FY ( y) is shown in Fig. 21.2.13. However, this probability can be evaluated from the Gaussian tables by first substituting pffiffiffi z ¼ y=s ¼ y=10 2 in the integral above and obtaining ð pffi ð 10 1 1 1= z (z2 =2) (1=2){y2 =200} P{Y(t) . 10} ¼ 1 pffiffiffiffi e dy ¼ 1 pffiffiffiffiffiffi e dz 20 p 1 2p 1 ffiffi p pffiffiffiffiffiffi Ð 1= 2 2 and from the tables P{Y 10} ¼ 1= 2p 1 e(z =2) dz ¼ 0:7603. Hence P{Y(t) . 10} ¼ 1 0:7603 ¼ 0:2397 Example 21.2.7 (Threshold Filter) This example is a computer simulation of using the threshold filter to retrieve from the output signal corrupted with white noise, the input
21.2
Random Processes through Linear Systems
589
FIGURE 21.2.14
sinusoidal signal in an environment of high signal-to-noise ratio (SNR). The input sinusoidal signal is given by 5 s(t) ¼ 2 sin (4v0 t) þ cos (7v0 t) þ 3 sin (7v0 t) 2 where v0 ¼ (2p=T0 ) ¼ (2p=64) and the period T0 ¼ 64. The input s(t) is discretized with Dt ¼ 1, and the discretized signal s(iDt) ¼ si, given by 2pi 5 2pi 2pi si ¼ 2 sin 8 þ cos 14 þ 3 sin 18 , 128 2 128 128
i ¼ 0, . . . ,127
is shown in Fig. 21.2.14. The number of points N is chosen to be N ¼ 27 ¼ 128 for ease in applying the fast Fourier algorithm (FFT) [36] to transform si into the frequency domain. The zero mean additive discrete white noise wi with variance 4 is shown in Fig. 21.2.15. The input power Psi is N 1 127 1X 1 X 2pi 5 2pi s2i ¼ 2 sin 8 þ cos 14 N i¼0 128 i¼0 128 2 128 2pi 2 þ 3 sin 18 ¼ 9:625 128
Ps i ¼
FIGURE 21.2.15
590
Random Processes and Linear Systems
FIGURE 21.2.16
and the noise power Pni is N1 127 1X 1 X w2i ¼ w2 ¼ 4 N i¼0 128 i¼0 i
Pn i ¼
and SNR ¼ 9:625=4 ¼ 2:406. The output signal yi, given by y i ¼ si þ w i ,
i ¼ 0, . . . , N 1
is shown in Fig. 21.2.16. The output power Pyi is given by P yi ¼
N 1 127 1X 1 X y2i ¼ y2 ¼ 14:811 N i¼0 128 i¼0 i
The input and the noise signals are transformed into the frequency domain by using the discrete Fourier transform (DFT) [36], defined by Sk ¼
N 1 X
si ejk(2p=N)i ¼
i¼0
Wk ¼
N 1 X
si ejk(2p=128)i
i¼0
wi ejk(2p=N)i ¼
i¼0
127 X
127 X
wi ejk(2p=128)i
i¼0
For the purely sinusoidal si the DFT Sk is given by Kro¨necker delta functions as N 5 2jdk8 þ dk14 3jdk18 Sk ¼ 2 2 where dm is the Kro¨necker delta equaling 1 when m ¼ 0 and 0 when m = 0. The DFT of the output yi is given by Yk ¼ Sk þ Wk ,
k ¼ 0, . . . , N 1
The input and the output spectra jSkj and jYkj are shown in Fig. 12.2.17. At frequncies of 8, 14, and 18, the peak values of the spectra are given in Table 21.2.1. In Fig. 21.2.17, if we choose those data points Yk that are above a threshold value a, then we can isolate all the peaks. A simple threshold filter can be designed by multiplying
21.2
Random Processes through Linear Systems
591
FIGURE 21.2.17
TABLE 21.2.1 f 8 14 18
Input Spectrum
Output Spectrum
jS8j ¼ 128 jS14j ¼ 160 jS18j ¼ 192
jY8j ¼ 145.47 jY14j ¼ 167.11 jY18j ¼ 200.04
fYk, k ¼ 0, . . . , N 2 1g by a unit step function u(jYkj 2 a), defined by 1, jYk j a u(jYk j a) ¼ 0, jYk j , a The output of this filter will be only those values of Yk that are above the threshold. If 55 a 145, then the output will be all three peaks. If 146 a 167, then the output will be only two peaks, and if 168 a 200, the output will be only one peak. Finally, if a . 200, the output will be zero. The output Zk of this filter will be given by Zk ¼ Yk u(jYk j a),
k ¼ 0, . . . , N 1
TABLE 21.2.2 k
jSkj
jYkj
u(jYkj 2 a)
jZkj
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0 0 0 128 0 0 0 0 0 160 0 0 0 192 0 0
19.709 5.858 11.158 145.473 11.091 23.615 28.109 23.032 15.388 167.111 15.412 10.366 22.076 200.036 17.753 32.344
0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0
0 0 0 145.473 0 0 0 0 0 167.111 0 0 0 200.036 0 0
592
Random Processes and Linear Systems
FIGURE 21.2.18
To clarify the operation of the threshold filter, the quantities, jSkj, jYkj, u(jYkj 2 a), and jZkj are tabulated in Table 21.2.2. The inverse DFT of Zk yields the signal zi that will approximate the input signal si: IDFT{Zk } ¼ zi si With a ¼ 65, this filter as applied and the IDFT of Zk yields the retrieved signal zi shown in Fig. 21.2.18 along with the superposed original signal si. We see from the figure that the retrieved signal is almost the same as the original signal. The power Pzi of the retrieved signal is Pzi ¼
B 21.3
N 1 127 1X 1 X z2i ¼ z2 ¼ 10:877 N i¼0 128 i¼0 i
LINEAR FILTERS
The previous section dealt with only lowpass filters. Among ideal filters we can define lowpass, highpass, and bandpass filters. The transfer functions and impulse responses of these filters are defined as follows: Ideal Lowpass: Hlow (v) ¼
sin (vB t) jvj vB ; hlow (t) ¼ otherwise pt
(21:3:1)
sin (vB t) jvj . vB ; hhigh (t) ¼ d(t) otherwise pt
(21:3:2)
1, 0,
Ideal Highpass: Hhigh (v) ¼
1, 0,
Ideal Bandpass: Hband (v) ¼
1,
jv vc j vB
0,
otherwise
sin (vB t) cos (vc t), hband (t) ¼ pt
; (21:3:3)
vc . 2vB
In these equations vB is the cutoff frequency and vc is the carrier frequency. The transfer functions and the impulse responses are shown in Fig. 21.3.1.
21.3
Linear Filters
593
FIGURE 21.3.1
As seen in the figures, the impulse responses are noncausal and hence the filters are not physically realizable. All physically realizable transfer functions must satisfy the Paley – Wiener criterion [45], given by ð1 ln jH(v)j dv , 1 (21:3:4) 2 1 1 þ v Nevertheless, these transfer functions can be approximated by physically realizable transfer functions. Example 21.3.1 A random telegraph wave X(t) with mean mX ¼ 0 and autocorrelation function RX(t) ¼ Ke 2ajtj is multiplied with a carrier signal cos(vct þ F), where F is a random phase independent of X(t) and K ¼ 2, a ¼ 3, and vc ¼ 5p. We will calculate the statistics of the output Y(t) ¼ X(t)cos(vct þ F) and devise a scheme to retrieve the input signal. Using the independence of X(t) and F, we have mY ¼ mX E[cos(vct þ F)] ¼ 0 since mX and E[cos(vct þ F)] ¼ 0. Again, from the independence of X(t) and F, the autocorrelation function RY (t) can be calculated as follows: RY (t) ¼ E½Y(t)Y(t þ t) ¼ E½X(t)X(t þ t)E½cos (vc t þ F) cos½vc (t þ t) þ F 1 ¼ E½X(t)X(t þ t) E½cos (vc t) þ cos½v0 (2t þ t) þ 2F 2 1 K ¼ RX (t) cos (vc t) ¼ eajtj cos (vc t) 2 2
594
Random Processes and Linear Systems
FIGURE 21.3.2
Hence the random process Y(t) is also stationary. The two autocorrelation functions RX(t) and RY (t) are shown in Fig. 21.3.2. The psd SX(v) of the input process X(t) is given by
SX (v) ¼
2Ka a2 þ v2
From the form of RY (t), we conclude that Y(t) is a bandpass signal, and the psd SY (v) of the output process Y(t) is given by 1 SX (v) p½d(v þ vc ) þ d(v vc ) 2p 2 1 ¼ ½SX (v þ vc ) þ SX (v vc ) 4 2Ka 1 1 ¼ þ 4 a2 þ (v þ vc )2 a2 þ (v vc )2
SY (v) ¼
The first step in retrieving the original signal X(t) is to multiply Y(t) by a carrier cos(vct þ Q) where Q is another random phase angle independent of Y(t). Thus, Z(t) ¼ Y(t)cos(vct þ Q), and the autocorrelation RZ(t) is given by RZ (t) ¼ E½Z(t)Z(t þ t) ¼ E½Y(t)Y(t þ t)E½cos (vc t þ Q) cos½vc (t þ t) þ Q 1 ¼ E½Y(t)Y(t þ t) E½cos (vc t) þ cos½vc (2t þ t) þ 2Q 2 1 K Keajtj ½1 þ cos (2vc t) ¼ RY (t) cos (vc t) ¼ eajtj cos2 (vc t) ¼ 2 4 8 The two autocorrelation functions RY(t) and RZ(t) are shown in Fig. 21.3.3.
21.3
Linear Filters
595
FIGURE 21.3.3
Clearly, Z(t) is also a stationary process. Its psd is given by 1 SY (v) p½d(v þ vc ) þ d(v vc ) 2p 2 1 ¼ ½SY (v þ vc ) þ SY (v vc ) 4 Ka 1 1 2 ¼ þ þ 8 a2 þ (v þ 2vc )2 a2 þ (v 2vc )2 a2 þ v2
SZ (v) ¼
The power spectral densities SX(v), SY(v), and SZ(v) are shown in Fig. 21.3.4. The maximum value of SX(v) occurs at zero frequency and is equal to SX;max ¼ SX (v)jv¼0 ¼
FIGURE 21.3.4
4 3
596
Random Processes and Linear Systems
The maximum value of SY (v) occurs at frequencies vc ¼ +5p and is equal to, 2 9 þ 50p2 ¼ 0:3364 SY, max ¼ SY (v)jv¼+5p ¼ 3 9 þ 100p2 The value of SZ(v) occurring at zero frequency is equal to SZ (v)jv¼0 ¼
1 9 þ 50p2 ¼ 0:1682 3 9 þ 100p2
and the values of SZ(v) occurring at frequencies of +10p are equal to SZ (v)jv¼+10p ¼
1 81 þ 3150p2 þ 10,000p4 ¼ 0:085 3 (9 þ 100p2 )(9 þ 400p2 )
As shown in Fig. 21.3.4, passing the random process Z(t) through a lowpass filter of bandwidth 2vc and amplitude equal to 1 9 þ 50p2 ¼ 0:1682 3 9 þ 100p2 multiplied by a suitable gain factor will reconstruct the original signal X(t). Matched Filters
In the previous section we discussed basic filtering methods where a causal transfer function H(v) was used to shape an input random process X(t) to any desired output random process Y(t). We will take this process one step further where we want to design an optimum filter for any given signal corrupted by noise with known statistics. There are three main factors in designing optimum systems: 1. Specifications of the input or a minimum knowledge of the characteristics of the input. We will assume that the input to the filter consists of a known deterministic signal s(t) with an additive stationary Gaussian noise N(t). The autocorrelation of the noise is RN (t) with psd FN(v): X(t) ¼ s(t) þ N(t),
E½N(t)N(t þ t) ¼ RN (t)
(21:3:5)
2. The nature of the impulse response h(t) such as linear time-invariant, linear time-varying, nonlinear, or causal, must be specified. We will consider linear time-invariant impulse responses, not necessarily causal. In this case the transfer function will be given by H(v). 3. The criterion of optimality has to specified. Some of the criteria of optimality similar the criteria of estimation discussed in Section 18.1 are (a) Maximum SNR (b) Minimum mean-square error (c) Bayesian optimality criteria such as maximum a posteriori and maximum likelihood The criterion of optimality chosen will be maximizing the signal-to-noise ratio (SNR). In the schematic shown in Fig. 21.3.5, the signal s(t) is corrupted with an additive stationary Gaussian noise N(t) with autocorrelation function RN(t). The input to the system is X(t), given by Eq. (21.3.5). We have to find the optimum impulse response h(t), not necessarily causal, such that the output SNR go(t) at the time of observation T is maximized.
21.3
Linear Filters
597
FIGURE 21.3.5
Since the system is linear, the output signal Y(t) will be given by Y(t) ¼ so (t) þ No (t)
(21:3:6)
where the output signal so(t) and the output noise No(t) will be convolutions of the noncausal impulse response h(t) with the input signal s(t) and the input noise N(t). Or ð1 ð1 s(t)h(t t)dt : No (t) ¼ N(t)h(t t)dt (21:3:7) so (t) ¼ 1
1
The instantaneous input and output SNRs gi(t) and go(t) can be defined as follows: gi (t) ¼
js2 (t)j js2 (t)j js2o (t)j js2o (t)j ¼ ; g ¼ (t) ¼ o E½N 2 (t) RN (0) E½No2 (t) RNo (0)
(21:3:8)
Since So (v) ¼ S(v)H(v), we can express so(t) as ð 1 1 S(v)H(v)e jvt dv so (t) ¼ 2p 1
(21:3:9)
From Eq. (21.3.9) we can relate the output noise psd FNo (v) to the input noise psd FN(v) as FNo (v) ¼ jH(v)j2 FN (v) We can express RNo (0) in terms of Eq. (21.3.10) as ð ð 1 1 1 1 2 jvt RN (0) ¼ jH(v)j FN (v)e dv ¼ jH(v)j2 FN (v)dv 2p 1 2p 1 t¼0
(21:3:10)
(21:3:11)
Substituting Eqs. (21.3.10) and (21.3.11) in the output SNR go(t) in Eq. (21.3.8), we have 1 Ð1 Ð1 2 2 jvt p 1 1 S(v)H(v)e jvt dv 1 S(v)H(v)e dv 2 Ð1 go (t) ¼ 1 Ð 1 ¼ (21:3:12) 2 2p 1 jH(v)j2 FN (v)dv 2 p 1 jH(v)j FN (v)dv The problem now is to find the transfer function H(v) in Eq. (21.3.12) that maximizes the output SNR go(t) without regard to causality. We will address the question of causality later. The Schwarz inequality [Eq. (14.5.2)] modified in the frequency form given by Eq. (21.3.13) will be used to solve for H(v): 2 ð 1 ð1 ð1 2 A(v)B (v)dv jA(v)j dv jB(v)j2 dv (21:3:13) 1
1
1
with equality holding when A(v) ¼ KB(v). We define the following quantities: pffiffiffiffiffiffiffiffiffiffiffiffiffi A(v) ¼ H(v) FN (v)
and
S(v)e jvt B (v) ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi FN (v)
(21:3:14)
598
Random Processes and Linear Systems
Substituting Eq. (21.3.14) in Eq. (21.3.13), Schwartz’ inequality becomes 2 2 ð 1 ð 1 ð 1 S(v) jvt 2 dv H(v)S(v)e dv H (v)FN (v)FN (v)dv 1 1 1 FN (v) or Ð1 2 ð 1 jvt S(v) 2 1 H(v)S(v)e dv Ð1 dv 2 1 FN (v) 1 H (v)FN (v)dv
(21:3:15)
Substituting Eq. (21.3.15) in Eq. (21.3.12), we obtain Ð1 2 2 ð 1 1 S(v)H(v)e jvt dv 1 1 S(v) dv go (t) ¼ Ð 2p 1 H(v) 2 FN (v)dv 2p 1 FN (v)
(21:3:16)
1
The equality holds when A(v) ¼ KB(v), or pffiffiffiffiffiffiffiffiffiffiffiffiffi S (v)ejvt H(v) FN (v) ¼ K pffiffiffiffiffiffiffiffiffiffiffiffiffi FN (v)
and
S (v)ejvt Hopt (v) ¼ K FN (v)
(21:3:17)
If T is the time of observation, then the optimum transfer function will be given by S (v)ejvT Hopt (v) ¼ K FN (v)
(21:3:18)
where K is an arbitrary real gain constant. Since Hopt(v) is matched to the input signal, it is called a matched filter. Under these optimum conditions the maximum output SNR go;max for the time of observation T becomes go, max
1 ¼ 2p
2 ð 1 S(v) 1
FN (v)
dv
(21:3:19)
Simplifications in Eq. (21.3.18) can result if the noise N(t) is white with psd FN(v) ¼ N0/2 and the gain constant is adjusted to be equal to K ¼ N0/2. In this case the optimum transfer function is given by Hopt (v) ¼ S (v)ejvT
(21:3:20)
The optimum impulse response is the IFT of Hopt(v) and is given by hopt (t) ¼ s(T t)
(21:3:21)
This equation shows clearly that the optimum output impulse response hopt(t) is matched to the input signal s(t). The autocorrelation of the signal s(t) is defined by ð1 Rs (t) ¼ s(t) s( t) ¼ s(t t)s(t)dt (12:3:22) 1
Taking FT on both sides of Eq. (12.3.22), we obtain FT½Rs (t) ¼ Fs (v) ¼ S(v)S (v) ¼ jS(v)j2
(12:3:23)
21.3
Linear Filters
599
jS(v)j2 is called the energy spectral density. The autocorrelation function Rs(t) can be given as the IFT of jS(v)j2: ð 2 1 1 Rs (t) ¼ S(v) e jvt dv (12:3:24) 2p 1 The optimum output signal spectrum So(v) under the condition K ¼ N0/2 is given by the product So (v) ¼ S(v)Hopt (v) ¼ S(v)S (v)ejvT ¼ jS(v)j2 ejvT Taking the IFT of So(v), the optimum output signal so(t) is given by ð 2 1 1 S(v) ejvT e jvt dv ¼ Rs (t T), t 0 so (t) ¼ 2p 1
(21:3:25)
(21:3:26)
Thus, the optimum output signal so(t) under the conditions of additive white noise is the autocorrelation of the input signal Rs(t) shifted by the time of observation T. The corresponding maximum SNR is given by ð 1 2 1 go, max ¼ jS(v)j2 dv (21:3:27) 2p N0 1 Ð1 Ð1 Using Parseval’s theorem (1/2p) 1 jS(v)j2 dv ¼ 1 s2 (t)dt in Eq. (21.3.27), we obtain ð 2 1 2 2Es go, max ¼ s (t)dt ¼ (21:3:28) N0 1 N0 where Es is the input energy. Summary of Matched-Filters Properties
Some of the properties of matched filters under the assumptions that (1) the additive noise N(t) is white with psd SN(v) ¼ N0 =2 and (2) the gain constant K ¼ N0/2 can be summarized as follows: 1. The Fourier spectrum So(v) of the output signal so(t) is equal to the energy spectral density jS(v)j2 multiplied by a delay factor e 2jvT of the input signal s(t). This can be shown from Eq. (21.3.20) as follows: So (v) ¼ S(v)Hopt (v) ¼ S(v)S (v)ejvT ¼ jS(v)j2 ejvT
(21:3:29)
2. The output response so(t) is equal to the autocorrelation Rs(t) of the input signal shifted in time by T as follow: so (t) ¼ Rs (t T),
t0
3. From Eq. (21.3.28), the optimum SNR go,max is ð ð 1 1 Fs (v) 1 2 1 1 2Es dv ¼ go, max ¼ Fs (v)dv ¼ 2p 1 FN (v) 2p No 2p 1 N0
(21:3:26)
(21:3:30)
4. Causality: If the original signal s(t) is time limited to (0,T ), then the matched filter given by s(T 2 t) is causal. In the following three examples (Examples 21.3.2 –21.3.4) the additive noise N(t) is white with psd SN(v) ¼ N0/2 and the gain constant K ¼ N0/2.
600
Random Processes and Linear Systems
The signal s(t) is a rectangular pulse defined by
Example 21.3.2 (Rectangular Pulse) s(t) ¼
A, 0,
0,t,T otherwise
From the tables, the FT of the signal s(t) is given by S(v) ¼ AT
sin (vT=2) jvT=2 e (vT=2)
Hence, from Eq. (21.3.20) the optimum transfer function is Hopt (v) ¼ AT
sin (vT=2) jvT=2 jvT sin (vT=2) jvT=2 e e e ¼ AT (vT=2) (vT=2)
and the impulse response hopt(t) ¼ s(T 2 t) ¼ s(t) and the matched filter are the same as the input signal. The autocorrelation function of the input from Eq. (12.3.22) is 2 A (T þ t), T , t 0 Rs (t) ¼ A2 (T t), 0,tT The output so(t) is obtained from Eq. (12.3.26) as follows: 2 A t, 0,tT Rs (t T) ¼ A2 (T t), T , t 2T From Eq. (21.3.26), the maximum output SNR is go, max
2 ¼ N0
ð1
2 s (t)dt ¼ N0 1 2
ðT 0
A2 dt ¼
2A2 T N0
Example 21.3.3 (Sinusoidal Pulse) The input signal s(t) is a time-limited sinusoidal pulse with energy Es ¼ 1 as given below: 2 rffiffiffiffi 2 2pn sin (v0 t), 0 t T, v0 ¼ 6 s(t) ¼ 4 T T 0, otherwise The signal s(t) is shown in Fig.(21.3.6a). It is simpler to approach the problem from the time domain. From Eq. (21.3.21) the optimum impulse response is given by 2 rffiffiffiffi 2 2pn v0 ¼ sin½v0 (T t), 0 t T, 6 hopt (t) ¼ s(T t) ¼ 4 T T 0, otherwise The optimum output signal so(t) is given by the convolution integral so (t) ¼ s(t) s(T t) ¼
ð1 s(t)s(T t þ t)dt 1
21.3
Linear Filters
FIGURE 21.3.6
The convolution integral can be written as follows: 2 Ð 2 t 6 T 0 sin (v0 t) sin½v0 (T t þ t)dt, 6 6 so (t) ¼ 6 2 Ð T sin (v0 t) sin½v0 (T t þ t)dt, 6 4 T tT 0, The integrals are evaluated, and the results are 2 1 ½2v0 t cos (v0 t) sin (v0 t), 6 v0 T 6 6 so (t) ¼ 6 1 ½v (2T t) cos (v0 t) þ sin (v0 t), 6 4 v0 T 0 0, The output signal so(t) is shown in Fig. (21.3.6b).
0tT T t 2T otherwise
0tT T t 2T otherwise
601
602
Random Processes and Linear Systems
It is also instructive to evaluate the output so(t) as the shifted version of the autocorrelation of the input signal s(t) as given in Eq. (21.3.26). The autocorrelation Rs(t) of the input s(t) is given by the following integrals: 2 Ð 2 tþT 6 T 0 sin½(v0 (t t) sin (v0 t)dt, T t 0 6 6 Rs (t) ¼ 6 2 Ð T 0tT 6 t sin½v0 (t t) sin (v0 t)dt, 4T 0, otherwise In this example the integrand in the integral shown above is the same as those in so(t) except that the limits are different. Evaluation of these integrals results in 2 1 ½v (T þ t) cos (v0 t) sin (v0 t), T t 0 6 v0 T 0 6 6 Rs (t) ¼ 6 1 ½v (T t) cos (v0 t) þ sin (v0 t), 0tT 6 4 v0 T 0 0,
otherwise
and indeed Rs(t 2 T ) ¼ so(t) and its graph also superpose the graph of so(t) exactly in Fig. 21.3.6, thus validating property 2 of Eq. (21.3.26).
Example 21.3.4 signal, given by
In this example, the input s(t) is a more complicated time-limited " s(t) ¼
et sin 0,
pt 2
,
0t4 otherwise
The psd of noise N(t) is given by N0/2 ¼ 2. The time of observation T ¼ 4 The FT of s(t) is given by 2p S(v) ¼ ½1 e4(1þjv) 4(1 þ jv)2 þ p2 From Eq. (21.3.20) the optimum filter Hopt(v) is given by 2p Hopt (v) ¼ S (v)ejvT ¼ ½1 e4(1jv) e4jv 4(1 jv)2 þ p2 From Eq. (21.3.25) the output spectrum So(v) is given by So (v) ¼ jS(v)j2 ejvT ¼
4p2 ½1 2e4 cos (4v) þ e 8 4jv e 16(1 þ v2 )2 þ 8p2 (1 v2 ) þ p4
The amplitude spectra jS(v)j and jHopt(v)j are the same and are shown in Fig. 21.3.7 along with the amplitude spectrum of jSo(v)j. The impulse response hopt(t) corresponding to the transfer function Hopt(v) can be obtained from Eq. (21.3.21) as ð 1 1 hopt (t) ¼ S (v)ejv(4t) dv 2p 1 ð 1 1 p(4 t) S (v)e jv(4t) dv ¼ s(4 t) ¼ e(4t) sin ¼ , 0t4 2p 1 2
21.3
Linear Filters
603
FIGURE 21.3.7
It is easier to obtain the output signal so(t) from the shifted version of the autocorrelation Rs(t) of the input s(t) rather than from the IFT of So(v). Rs(t) can be obtained from the following integrals: 2 Ð Tþt
pt p(t t) dt, sin 6 0 2 2 6 6Ð
T Rs (t) ¼ 6 6 e2tþt sin pt sin p(t t) dt, 6 t 2 2 4 e2tþt sin
0,
T t 0 0tT otherwise
After some involved algebra, these integrals can be evaluated as
pt
pt ½e(8þt) þ et þ p cos ½e(8þt) et , 2 2 6 p 6
pt
pt 6 Rs (t) ¼ (8t) t þ e p cos 2 sin ½e ½e(8t) et , 4(4 þ p2 ) 6 4 2 2 0, 2
2 sin
4 t 0 0 t 4 otherwise
and the output signal so(t) can be evaluated from the convolution integral or by shifting the autocorrelation Rs(t) by T ¼ 4: so (t) ¼ s(t) s(4 t) ¼ Rs (t 4) The signal s(t), the optimum impulse response hopt(t), the autocorrelation Rs(t), and the output signal so(t) are shown in Fig. 21.3.8. The maximum SNR from Eq. (21.3.19) is go, max ¼
1 2p
ð1
jS(v)j2 1 2 dv ¼ 2p N0 1 SN (v)
The input energy Es ¼ go,max ¼ 0.0889.
ÐT 0
ð1 1
jS(v)j2 dv ¼
2 N0
ðT 0
s2 (t)dt ¼
2Es N0
s2 (t)dt ¼ 0:17784, and with N0/2 ¼ 2, the maximum SNR is
604
Random Processes and Linear Systems
FIGURE 21.3.8
Example 21.3.5 (Nonwhite Gaussian Noise) In this example the signal s(t) and the psd FN(v) of the nonwhite stationary noise N(t) are given by 2 3 t 3t
e 2 e 2 , s(t) ¼ 4 4 0,
t0
FN (v) ¼
t,0
4 1 þ 4v2
The time of observation T is equal to 5, and the dc value of the optimum transfer function Hopt(0) ¼ 1. The Fourier spectrum of s(t) can be obtained from the tables as S(v) ¼
3 3 ¼ (1 þ 2jv)(3 þ 2jv) 3 4v2 þ 8jv
The transfer function Hopt(v) of the matched filter is found from Eq. (21.3.18) as Hopt (v) ¼ K
S (v)ejvT 3ejv5 4 þ v2 ¼K jFN (v)j 3 4v2 8jv 4
Since H(0) ¼ 1, we obtain K ¼ 1, and Hopt(v) is Hopt (v) ¼
3(4 þ v2 )ejv5 4(3 4v2 8jv)
The Fourier spectrum So(v) of the optimum output signal so(t) is given by So (v) ¼ S(v)Hopt (v) ¼
jS(v)j2 ejv5 9(4 þ v2 )e5jv ¼ FN (v) 4(16v4 þ 40v2 þ 9)
The amplitude spectra jS(v)j, jHopt(v)j, and jSo(v)j are shown in Fig. 21.3.9.
21.3
Linear Filters
605
FIGURE 21.3.9
The optimum impulse response hopt(t) and the output time function so(t) are obtained from the IFTs of their respective Fourier spectra Hopt(v) and So(v) and are given by 45 (t5)=2 21 3(t5)=2 3 e e u(5 t) d(5 t) hopt (t) ¼ 64 64 16 135 (t5)=2 21 3(t5)=2 e e so (t) ¼ u(5 t) 512 512 135 (5t)=2 21 3(5t)=2 e e u(t 5) þ 512 512 The input signal s(t), the impulse response hopt(t), and the output time function so(t) are shown in Fig. 21.3.10.
FIGURE 21.3.10
606
Random Processes and Linear Systems
The optimum SNR is obtained from Eq. (21.3.19) and is given by ð ð 1 1 jS(v)j2 1 1 9(4 þ v2 ) dv ¼ dv go, max ¼ 2p 1 FN (v) 2p 1 4(16v4 þ 40v2 þ 9) 1 135 1 21 57 1 tan (1) tan (1) ¼ ¼ 2p 128 128 256
B 21.4.
BANDPASS STATIONARY RANDOM PROCESSES
Review of Bandpass Signals
Bandpass signals form the backbone of signal transmission. The channel through which the signal is transmitted is bandwidth-limited to a band of frequencies centered about the carrier. If the bandwidth 2vB is much smaller than the carrier frequency vc, then such signals are called narrowband signals. They can be represented in three ways as 1. Natural Envelope Representation. This is the usual representation of a narrowband process and is given by s(t) ¼ a(t) cos½vc t þ u(t)
(21:4:1)
where a(t) (called the natural envelope) is a lowpass signal bandlimited to 2vB, vc is the carrier frequency, and u(t) is the phase angle. If the Fourier spectrum of a(t) is A(v), then the spectrum of the signal S(v) is given by 1 A(v) p½d(v þ vc ) þ d(v vc ) 2p 1 ¼ ½A(v þ vc ) þ A(v vc ) 2
S(v) ¼
(21:4:2)
The amplitude spectra of A(v) and S(v) are shown in Fig. 21.4.1. In Fig. 21.4.1 2vB , vc, and hence the signal s(t) is bandpass. The band of frequencies centered about vc is called the upper sideband, and the band centered about 2vc is called
FIGURE 21.4.1
21.4. Bandpass Stationary Random Processes
607
the lower sideband. Each of the sidebands carries the same information as the original lowpass signal s(t). 2. Quadrature Representation. The signal s(t) in Eq. (21.4.1) can be expanded and written as s(t) ¼ a(t) cos½vc t þ u(t) ¼ a(t)½cos (vc t) cos u(t) sin (vc t) sin u(t)
(21:4:3)
In Eq. (21.4.3) we can define the quadrature components sc(t) and ss(t) as sc (t) ¼ a(t) cos u(t), ss (t) ¼ a(t) sin u(t),
in phase quadrature
(21:4:4)
s(t) ¼ sc (t) cos u(t) ss (t) sin u(t)
(21:4:5)
and write Eq. (21.4.3) as
The envelope a(t) and the phase u(t) are obtained from the quadrature components as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ss (t) a(t) ¼ s2c (t) þ s2s (t); u(t) ¼ tan1 (21:4:6) sc (t) Since the signal is narrowband a(t) varies slowly compared to tc ¼ 2p=vc . 3. Analytic Signal Representation. Since both sidebands in a bandpass signal carry the same information, we should be able to transform the spectrum into positive frequencies. This is accomplished by using Hilbert transforms (HTs), [36]. The Hilbert transform s^ (t) of any signal s(t) is a time-domain transformation, defined by ð1 s(t) 1 s^ (t) ¼ s(t) ¼ dt (21:4:7) pt p(t t) 1 under the assumptions, that (a) s(t) is square integrable and (b) the FT S(v) is minimum phase. The improper integral in Eq. (21.4.7) is evaluated in the sense of Cauchy principal value, defined by ) (ð ð1 ð1 t1 s(t) s(t) s(t) dt ¼ lim dt þ dt 1!0 1 p(t t) 1 p(t t) tþ1 p(t t) The inverse Hilbert transform (IHT) is defined by ð1 1 s^ (t) s(t) ¼ ^s(t) ¼ dt pt p(t t) 1
(21:4:8)
Since (1=pt) , jsgn(v), the FT of s^ (t) is obtained by using the convolution property as ^ FT{^s(t)} ¼ S(v) ¼ S(v)j sgn(v) We can also define the HT of the Fourier transform of S(v) as, ð1 1 S(p) ^^ ^ FT{S(v)} ¼ S(v) ¼ S(v) ¼ dp pv p(v p) 1
(21:4:9)
(21:4:10)
608
Random Processes and Linear Systems
^^ ^ In general, S(v) = S(v), but if s(t) is a real causal time function, then it can be shown that ^^ ^ S(v) ¼ S(v)sgn(v)
(21:4:11)
The analytic signal zs(t) corresponding to s(t) is defined as zs ðtÞ ¼ sðtÞ þ j^sðtÞ
(21:4:12)
The Fourier spectrum of the analytic signal zs(t) can be obtained by using Eq. (21.4.9): Zs ðvÞ ¼ Sðv þ jðjÞSðvÞsgnðvÞ ¼ 2SðvÞuðvÞ
(21:4:13)
Thus the spectrum Zs(v) is causal in the frequency domain. In most problems it may be easier to find the HT from the frequency representation of Eq. (21.4.9) rather than the convolution integral of Eq. (21.4.7). Example 21.4.1
We will find the Hilbert transforms for the following functions:
(a) xc(t) ¼ a(t)cos(vct): The FT of xc(t) is Xc (v) ¼
1 1 A(v) p½d(v þ vc ) þ d(v vc ) ¼ ½A(v þ vc ) þ A(v vc ) 2p 2
and from Eq. (21.4.9) 1 j X^ c (v) ¼ ½A(v þ vc ) þ A(v vc )½jsgn(v) ¼ ½A(v þ vc ) A(v vc ) 2 2 Hence x^ c (t) ¼ a(t) sin (vc t) (b) xs(t) ¼ a(t) sin(vct): The FT of xs(t) is Xs (v) ¼
1 j A(v) jp½d(v þ vc ) d(v vc ) ¼ ½A(v þ vc ) A(v vc ) 2p 2
and from Eq. (21.4.9) j 1 X^ s (v) ¼ ½A(v þ vc ) A(v vc )½jsgn(v) ¼ ½A(v þ vc ) þ A(v vc ) 2 2 Hence x^ s (t) ¼ a(t) cos (vc t) (c) y(t) ¼ sin(vBt) sin(vct), vB vc: We will solve this problem from basics and show that the HT y^ (t) follows directly from the previous example (b). The
21.4. Bandpass Stationary Random Processes
Fourier spectrum Y(v) is obtained from the tables as 1 { jp(v þ vB ) d½v vB )} {jp½d(v þ vc ) d(v vc )} 2p p ¼ {d½v þ vB þ vc d½v þ vB vc 2
Y(v) ¼
d½v vB þ vc þ d½v vB vc } p ¼ {d½v þ (vc þ vB ) þ d½v (vc þ vB )} 2 p þ {d½v þ (vc vB ) þ d½v (vc vB )} 2 ^ From Eq. (21.4.9) the FT Y(v) is given by ^ Y(v) ¼ Y(v)( j sgn(v)) jp {d½v þ (vc þ vB ) d½v (vB þ vc )} 2 jp {d½v þ (vc vB ) d½v (vc vB )} ¼ 2
¼
The functions Y(v), 2jsgn(v), and Y(v) are shown in Fig. 21.4.2
FIGURE 21.4.2
609
610
Random Processes and Linear Systems
TABLE 21.4.1 No.
Table of Hilbert transforms Time Function s(t)
Hilbert Transform s^ (t)
1. 2. 3.
cos(vct) sin(vct) a(t)cos(vct)
sin(vct) 2cos(vct) a(t)sin(vct)
4.
a(t)sin(vct)
2a(t)cos(vct)
sin (vc t) vc t 1 1þ t2 1 1 u t u tþ 2 2
1 cos (vc t) vc t t 1 þ t2 1 t þ 1=2 ln p t 1=2 1 pt 2pd(t)
5. 6. 7. 8. 9.
d(t) 1 t
Comments
a(t) bandlimited to 2vB, ,vc a(t) bandlimited to 2vB, ,vc
Hence the HT of y(t) is 1 y^ (t) ¼ { sin½(vc vB )t sin½(vc þ vB )t} 2 ¼ sin (vB t) cos (vc t) A result that can be obtained by substituting a(t) ¼ sin(vBt) in example (b) above. A summary of some Hilbert transforms is given in Table 21.4.1. Substituting s(t) ¼ a(t) cos½vc t þ u(t) from Eq. (21.4.1) and s^ (t) ¼ a(t) sin½vc t þ u(t) from Example 21.4.1 in Eq. (21.4.12) for the analytic signal, we can write zs (t) ¼ a(t){ cos½vc t þ u(t) j sin½vc t þ u(t)} ¼ a(t)e j½vc tþu(t) ¼ ½a(t)e ju(t) e jvc (t) ¼ s~ (t)e jvc t
(21:4:14)
where s~ (t) ¼ a(t)eju(t) is defined as the complex envelope of the analytic signal zs(t). The bandpass signal s(t) can be written in terms of the analytic signal as s(t) ¼ Re½zs (t) ¼ Re½~s(t)e jvc t ¼ a(t) cos½vc t þ u(t)
(21:4:15)
Using the relationship Re[z] ¼ 12[z þ z ] we can rewrite Eq. (21.4.15) as s(t) ¼
1 s(t)e jvc t þ s (t)ejvc t 2
(21:4:16)
and its Fourier spectrum as 1 S(v) ¼ ½S(v vc ) þ S (v þ vc ) 2
(21:4:17)
The absolute value js˜(t)j of the complex envelope s˜(t) is the natural envelope a(t), and s˜(t) can be written in terms of the quadrature components of Eq. (21.4.4) as s~ (t) ¼ sc (t) þ jss (t)
(21:4:18)
21.4. Bandpass Stationary Random Processes
611
It should be noted that a(t), s˜(t), sc(t), and ss(t) are all lowpass signals, whereas s(t) and zs(t) are bandpass signals. Example 21.4.2 A narrowband bandpass signal X(t) with bandwidth 2vB is passed through a bandpass filter with bandwidth 2vB and impulse response h(t). We will show that the output of this system Y(t) can be represented by 1 Y(t) ¼ Re½zh (t) zX (t) 2 where zh(t) and zX(t) are the analytic signals corresponding to h(t) and X(t). We will now evaluate ð1 ð1 ^ ^ Re zh (t)zX (t)dt ¼ Re ½h(t) þ jh(t)½X(t) jX(t)dt 1
ð1 ¼
1
^ X(t)dt ^ ½h(t)X(t) þ h(t)
1
By Parseval’s theorem, we obtain ð1 ð 1 1 ^ ^h(t)X(t)dt ^ H(v)X^ (v)dv ¼ 2p 1 1 ð 1 1 j sgn(v)H(v) j sgn(v)X (v)dv ¼ 2p 1 ð 1 1 sgn2 (v)H(v) X (v)dv ¼ 2p 1 ð ð1 1 1 H(v) X (v)dv ¼ h(t)X(t)dt ¼ 2p 1 1 Hence ð 1 Re
zh (t)zX (t)dt
ð1
ð1
¼2
1
h(t)X(t)dt ¼ 2 1
Re½zh (t)Re½zX (t)dt
(A)
1
We will evaluate the output Y(t) given by the convolution integral ð1 Y(t) ¼ h(t)X(t t)dt 1
^ and zX (t) ¼ X(t) þ jX(t) ^ we have and with zh (t) ¼ h(t) þ jh(t) ð1 Re½zh (t)Re½zX (t t)dt
Y(t) ¼ 1
Substituting the result (A) derived earlier in this equation, we obtain ð1 1 1 Y(t) ¼ Re zh (t)zX (t t)dt ¼ Re½zh (t) zX (t) 2 2 1 Example 21.4.3 A pulsed radiofrequency (RF) wave x(t) ¼ ApT=2 (t) cos(vc t), where pT=2 is a pulse of width T defined by pT=2 ¼ u(t þ T=2) u(t T=2), is applied to an ideal bandpass filter shown as in Fig. 21.4.3.
612
Random Processes and Linear Systems
FIGURE 21.4.3
We want to find the output y(t) using Hilbert transform techniques. The HT of the input can be obtained form the HT table (Table 21.4.1) as x^ (t) ¼ ApT=2 (t) sin (vc t), the analytic signal and the complex envelope corresponding to x(t) are zx (t) ¼ ApT=2 (t)e jvc t ;
x~ (t) ¼ ApT=2 (t)e jt 0
and the FT of zx (t) is Zx (v) ¼ AT
sin½(v vc )T=2 (v vc )T=2
The equivalent lowpass filter for H(v) can be obtained by retaining the positive half of ~ H(v) and backshifting it to the origin. Thus H(v) and the corresponding complex ~ impulse response h(t) are given by ~ H(v) ¼ u(v þ vB ) u(v vB )e jv0 ;
~ ¼ vB sin (vB t) h(t) p vB t
The complex envelope y~ (t) of the output y(t) is the convolution given by ð T=2 vB sin (vB t) ~ ¼ ApT=2 (t) vB sin (vB t) ¼ A PT=2 ðt tÞdt y~ (t) ¼ x~ (t) h(t) vB t p vB t T=2 p Ðx Using the sine integral Si(x) ¼ 0 ðsin(t)=tÞdt, the preceding equation can be written as A T T Si vB t þ y(t) ¼ Si vB t p 2 2 The RF pulse x(t) is shown in Fig. 21.4.4a, and y(t) is shown in Fig. 21.4.4b. Bandpass Stationary Random Processes
A stationary random process N(t) is called a narrowband bandpass process if the power spectral density (psd) SN(v) satisfies two conditions: 1. SN(v) is zero outside a band of frequencies centered around the carrier frequency +vc. 2. The width 2vB of the band of frequencies is very much smaller than the carrier frequency vc. These conditions are similar to those of deterministic narrowband signals except that the Fourier spectrum has been replaced by the power spectral density. Similar to the deterministic process, these narrowband stationary processes can also be represented in three ways.
21.4. Bandpass Stationary Random Processes
613
FIGURE 21.4.4
The natural envelope representation is N(t) ¼ W(t) cos½vc t þ Q(t)
(21:4:19)
where W(t) is a lowpass stationary random process with psd SW(v) and bandwidth 2vB and Q(t) is a random phase. The quadrature representation can be obtained by expanding Eq. (21.4.19) as N(t) ¼ W(t)½cos (vc t) cos½Q(t) sin (vc t) sin½Q(t) and rewriting it as N(t) ¼ Nc (t) cos (vc t) Ns (t) sin (vc t)
(21:4:20a)
where Nc (t) ¼ W(t) cos½Q(t);
Ns (t) ¼ W(t) sin½Q(t)
(21:4:20b)
are the lowpass stationary quadrature components. The analytic signal representation is N(t) ¼ Re½zN (t) ¼ Re½N(t)e jvc t
(21:4:21)
614
Random Processes and Linear Systems
~ where zN (t), the analytic signal corresponding to N(t), and N(t), the complex envelope in terms of the quadrature components Nc(t) and Ns(t), are given by ^ zN (t) ¼ N(t) þ jN(t) ~ ¼ Nc (t) þ jNs (t) N(t)
(21:4:22)
^ is Hilbert transform of N(t). where N(t) Quadrature Representation
We will now investigate the quadrature representation of Eq. (21.4.20a). Since N(t) is stationary, the correlation functions of any two stationary random processes N(t) and M(t) are governed by the symmetry conditions E½N(t)N(t þ t) ¼ RN (t) ¼ E½N(t þ t)N(t) ¼ RN ( t) E½N(t)M(t þ t) ¼ RNM (t) ¼ E½M(t þ t)N(t) ¼ RMN ( t)
(21:4:23)
We will determine the autocorrelation functions RN(t), RNc (t), and RNs (t) and the crosscorrelation functions RNc Ns (t) and RNs Nc (t): RN (t) ¼ E{½Nc (t) cos (vc t) Ns (t) sin (vc t) ½Nc (t þ t) cos½vc (t þ t) Ns (t þ t) sin½vc (t þ t)} ¼ RNc (t) cos (vc t) cos½vc (t þ t) þ RNs (t) sin (vc t) sin½vc (t þ t) RNc Ns (t) cos(vc t) sin½vc (t þ t) RNs Nc (t) sin(vc t) cos½vc (t þ t)
(21:4:24)
Using trigonometric identities, we obtain 1 RN (t) ¼ ½RNc (t) þ RNs (t) cos (vc t) 2 1 þ ½RNc (t) RNs (t) cos½vc (2t þ t) 2 1 ½RNc Ns (t) RNs Nc (t) sin (vc t) 2 1 ½RNc Ns (t) þ RNs Nc (t) sin½vc (2t þ t) 2
(21:4:25)
Since there can be no dependence on t for a stationary process, the following conditions must be satisfied by N(t) in Eq. (21.4.25): RNc (t) ¼ RNs (t) RNc Ns (t) ¼ RNs Nc (t)
(21:4:26)
Substituting Eqs. (21.4.6) in Eq. (21.4.25), we obtain the autocorrelation RN(t) also in quadrature form as RN (t) ¼ RNc(t) cos(vc t) RNc Ns (t) sin (vc t)
(21:4:27)
^ of the HT of N(t), but it is In a similar manner we can also derive the autocorrelation N(t) easier to use the HT table (Table 21.4.1) and write RN^ (t) ¼ RNc (t) sin(vc t) þ RNc Ns (t) cos (vc t)
(21:4:28)
21.4. Bandpass Stationary Random Processes
615
Equations (21.4.27) and (21.4.28) can be combined as a matrix and written as RN (t) RNc (t) cos (vc t) sin (vc t) (21:4:29) ¼ RN^ (t) RNc Ns (t) sin (vc t) cos (vc t) and the correlation functions of quadrature components Nc(t) and Ns(t) can be given in ^ as terms of the correlation functions of N (t) and N(t) RNc (t) cos (vc t) sin (vc t) RN (t) ¼ (21:4:30) RNc Ns (t) sin (vc t) cos (vc t) RN^ (t) Equations (21.4.29) and (21.4.30) are orthogonal transformations.
Analytic Signal Representation
We will investigate the analytic signal representation of the narrowband bandpass stationary random process N(t). The analytic signal corresponding to N(t) is jvc t ^ ¼ N(t)e ~ zN (t) ¼ N(t) þ jN(t)
(21:4:31)
~ will be given by and the complex envelope N(t) jvc t ~ ¼ Nc (t) þ jNs (t) ¼ zN (t)ejvc t ¼ ½N(t) þ jN(t)e ^ N(t)
(21:4:32)
where Nc(t) and Ns(t) are the lowpass quadrature components given by Eq. (21.4.20b). The quadrature components Nc(t), Ns(t) can be expressed in terms of the components of ^ by expanding Eq. (21.4.32) as the analytic signal N(t), N(t) ^ Nc (t) þ jNs (t) ¼ ½N(t) þ jN(t)½cos (vc t) j sin (vc t) ^ sin (vc t) þ j½N(t) ^ cos (vc t) N(t) sin (vc t) ¼ N(t) cos (vc t) þ N(t) and equating real and imaginary parts to obtain, in matrix form Nc (t) sin (vc t) cos (vc t) Nc (t) ¼ Ns (t) sin (vc t) cos (vc t) N^ s (t)
(21:4:33)
Inverting the matrix equation [Eq. (21.4.33)] the components of the analytic signal, N(t), ^ are expressed in terms of the quadrature components N(t) cos (vc t) sin (vc t) Nc (t) N(t) ¼ (21:4:34) ^ N(t) sin (vc t) cos (vc t) Ns (t) Equations (21.4.33) and (21.4.34) are again orthogonal transformations.
Properties of Quadrature Components
Without loss in generality we shall assume that N(t) is a zero mean stationary narrowband bandpass Gaussian process. ^ 1. Since N(t) is Gaussian, N(t), which is linear transformation, is also a zero mean stationary narrowband bandpass Gaussian process. ^ and 2. From Eq. (21.4.33), Nc(t) and Ns(t) are linear transformations of N(t) and N(t) hence they are also stationary Gaussian processes.
616
Random Processes and Linear Systems
3. Because of the stationarity of N(t), Eq. (21.4.26) must be satisfied. RNc (t) ¼ RNs (t) RNc Ns (t) ¼ RNs Nc (t)
(21:4:26)
4. Since RNc (t) ¼ RNs (t), Nc (t) and Ns (t) have the same psd SNc (v) ¼ SNs (v). 5. Substituting t ¼ 0 in Eq. (21.4.27), we obtain RN (0) ¼ RNc (0) and from Eq. (21.4.26) RN (0) ¼ RNc (0) ¼ RNs (0) ¼ s2N
(21:4:35)
Thus, the original random process N(t) and its quadrature components Nc(t) and Ns(t) have the same variance s2N . 6. The autocorrelation function RN (t) will be computed: RN (t) ¼ E{½Nc (t) þ jNs (t)½Nc (t þ t) jNs (t þ t)} ¼ RNc (t) þ RNs (t) j½RNc Ns (t) RNs Nc (t) ¼ 2½RNc (t) jRNc Ns (t) ½from Eq. (21:4:26)
(21:4:36)
Multiplying Eq. (21.4.36) by ejvc t , we obtain RN (t)ejvc t ¼ 2½RNc (t) jRNc Ns (t)½cos (vc t) j sin (vc t) ¼ 2½RNc (t) cos (vc t) RNc Ns sin (vc t) 2j½RNc (t) sin (vc t) þ RNc Ns (t) cos (vc t)þ ¼ 2½RN (t) jRN^ (t)
½ from Eq. (21:4:29)
(21:4:37)
Taking the real parts of both sides of Eq. (21.4.37) results in 1 RN (t) ¼ Re½RN (t)ejvc t 2
(21:4:38)
7. The symmetry condition of any cross-correlation function is RNc Ns (t) ¼ RNs Nc ( t), and together with the stationarity condition of Eq. (21.4.26), RNc Ns (t) ¼ RNs Nc (t) implies that RNc Ns (t) ¼ RNs Nc ( t) ¼ RNs Nc (t)
(21:4:39)
Hence the cross-correlation function RNc Ns (t) is odd. 8. The autocorrelation function RN (t) has Hermitian symmetry. From Eq. (21.4.36) and the fact that RNc Ns (t) is odd, from Eq. (21.4.39), we obtain RN ( t) ¼ 2½RNc (t) þ jRNc Ns (t) ¼ 2½RNc (t) jRNc Ns (t), ¼ RN (t) showing Hermitian symmetry.
RNc Ns (t) odd function (21:4:40)
21.4. Bandpass Stationary Random Processes
617
9. The cross-spectral density SNc Ns (v) is purely imaginary, as shown below: ð1 ð1 jvc t SNc Ns (v) ¼ RNc Ns (t)e dt ¼ RNc Ns ( t)ejvc t dt 1 ð1
1
¼ 1
RNc Ns (t)e jvc t dt ¼ SNc Ns (v)
(21:4:41)
The only way that Eq. (21.4.41) can be satisfied is when Re½SNc Ns (v) ¼ 0. 10. Since RNc Ns (t) is odd from, Eq. (21.4.39), we have RNc Ns (0) ¼ 0. Hence E½Nc (t)Ns (t) ¼ 0; showing that the quadrature components Nc(t) and Ns(t) are orthogonal. Since they are zero mean, they are uncorrelated, and because they are Gaussian, they are also independent. 11. Structure of SN (v) and SN (v). Since RN (t) has Hermitian symmetry, from Eq. (21.4.40), its FT SN (v) is a real-valued function of the frequency v. Using the relationship Re½z ¼ (z þ z )=2 in Eq. (21.4.38), we can write 1 RN (t) ¼ ½RN (t)ejvc t þ RN (t)e jvc t 4
(21:4:42)
Taking FT on both sides of Eq. (21.4.42), we have 1 SN (t) ¼ ½SN (v vc ) þ SN (v þ vc ) 4
(21:4:43)
12. Structure of SNc (v) ¼ SNc (v). We will assume that the bandwidth 2vB of SNc ðvÞ is governed by –vB vc. vB. From Eq. (21.4.30) we have RNc (t) ¼ RNs (t) ¼ RN (t) cos(vc t) þ RN^ (t) sin(vc t)
(21:4:44)
Taking FT on both sides of Eq. (21.4.14), we obtain 1 SN (v) p½d(v þ vc ) þ d(v vc ) 2p j SN (v)sgn(v) jp½d(v þ vc ) (v vc ) 2p 1 ¼ SN (v þ vc )½1 þ sgn(v þ vc ) 2 1 þ SN (v vc )½1 sgn(v vc ), vB vc vB 2 (21:4:45)
SNc (v) ¼ SNs (v) ¼
In Eq. (21.4.45) the terms containing the signum functions are given by 2, v . vc 0, v . vc ; ½1 sgn(v vc ) ¼ (21:4:46) ½1 þ sgn(v þ vc ) ¼ 0, v vc 2, v vc Substituting Eq. (21.4.46) in Eq. (21.4.45), we obtain SNc (v) ¼ SNs (v) ¼ SN (v þ vc )u(v þ vc ) þ SN (v vc )u½(v vc ), vB vc vB
(21:4:47)
The development of the psd SNc (v) from the psd SN(v) is shown in Fig. 21.4.5.
618
Random Processes and Linear Systems
FIGURE 21.4.5 Natural Envelope Representation
The natural envelope representation of a narrowband bandpass stationary random process is given by N(t) ¼ W(t) cos½vc t þ Q(t)
(21:4:19)
where W(t) in terms of the quadrature components Nc(t) and Ns(t) is given by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi W(t) ¼ Nc2 (t) þ Ns2 (t) (21:4:48) and the phase angle Q(t), by Q(t) ¼ tan1
Ns (t) Nc (t)
(21:4:49)
If N(t) is Gaussian-distributed, then from property 2 above, the quadrature components Nc(t) and Ns(t) are also Gaussian-distributed. We will assume that they are zero mean. Under these conditions we want to find the distributions of the natural envelope W(t) and the phase angle Q(t). By property 10, Nc(t) and Ns(t) are independent, and by property 5, their variance s2N is the same as the variance of N(t). Hence their joint
21.4. Bandpass Stationary Random Processes
619
density can be given as follows: fNc Ns (nc , ns ) ¼
1 2 2 2 e(1=2)½(nc þns )=sN 2 2psN
(21:4:50)
The transformation between Nc(t) and Ns(t) to W(t) is given by Nc (t) ¼ W(t) cos½Q(t) Ns (t) ¼ W(t) sin½Q(t)
(21:4:51)
The pdf fW(w) can be found by the techniques described in Section 13.3. The Jacobian of the transformation is 2 3 @Nc (w, u) @Nc (w, u) 6 7 cos (u) w sin(u) @u (21:4:52) kJ(w, u)k ¼ 4 @w ¼ 5 sin (u) w cos(u) @Ns (w, u) @Ns (w, u) @w @u or kJ(w, u)k ¼ w½cos2 (u) þ sin2 (u) ¼ w The solutions for w and u are qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi w ¼ n2c þ n2s ;
ns u ¼ tan nc 1
(21:4:53)
Hence, from Eq. (13.3.5), we obtain fNc Ns (nc , ns ) ¼
1 w (1=2)(w2 =s2N ) 2 2 2 e(1=2)½(nc þns )=sN ¼ e ¼ fW, Q (w, u) 2ps2N 2ps2N
(21:4:54)
which is the desired joint density fW, Q (w;u). Integrating Eq. (21.4.54) with respect to u, we obtain ð 2p w (1=2)(w2 =s2N ) w 2 2 fW (w) ¼ e du ¼ 2 e(1=2)(w =sN ) , w . 0 (21:4:55) 2 2ps s 0 N N which is a Rayleigh distribution shown in Fig. 7.8.2. Similarly, integrating with respect to w, we obtain ð 1 2p w (1=2)(w2 =s2N ) 1 , 0 u 2p (21:4:56) fQ (u) ¼ e du ¼ 2p 0 s2N 2p which is a uniform distribution in (0, 2p). Thus we find that W(t) and Q(t) are also independent. This derivation holds good only if the mean values of the quadrature components Nc(t) and Ns(t) are zero. We will now discuss the case where the means are not zero. Narrowband Bandpass Stationary Process with Additive Sinusoid
A stationary bandpass random process X(t) is given by X(t) ¼ N(t) þ a cos½vc t þ C
(21:4:57)
where N(t) is a zero mean Gaussian stationary narrowband bandpass random process defined by Eq. (21.4.19) with quadrature components Nc(t) and Ns(t), defined by Eq. (21.4.51). C is a random phase uniformly distributed in (0, 2p), and a is a constant.
620
Random Processes and Linear Systems
The variances of N(t), Nc(t), and Ns(t) are all equal to s2N . The natural envelope of X(t) will be given by X(t) ¼ R(t) cos½vc t þ F(t)
(21:4:58)
where F(t) is some function of both C and Q(t). Expanding Eq. (21.4.58), we obtain X(t) ¼ R(t)½cos½F(t) cos(vc t) sin½F(t) sin(vc t)
(21:4:59)
Substituting Eq. (21.4.20a) for N(t) in Eq. (21.4.57), we obtain X(t) ¼ ½Nc (t) þ a cos(C) cos(vc t) ½Ns (t) þ a sin(C) sin(vc t)
(21:4:60)
and the quadrature components of X(t) can be defined as Xc (t) ¼ Nc (t) þ a cos(C), in phase Xs (t) ¼ Ns (t) þ a sin(C), quadrature
(21:4:61)
Unlike Nc(t) and Ns(t), which are zero mean Gaussian lowpass random processes, Xc(t) and Xs(t) are Gaussian but not zero mean processes. Their mean values are given by mXc ¼ a cos(C);
mXs ¼ a sin(C) and
a¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m2Xc þ m2Xs
(21:4:62)
Without loss of generality, we can assume that C ¼ c is a constant and the quadrature components Xc(t) and Xs(t) are independent. Under these assumptions their joint Gaussian density is fXc Xs (xc , xs ) ¼
2 2 1 2 e(1=2sN )½xc a cos(c) þ½xs a sin(c) 2 2psN
1 2 2 2 2 ¼ e(1=2sN ){xc þxs þa 2a½xc cos(c)þxs sin(c)} 2ps2N
(21:4:63)
Comparing terms in Eqs. (21.4.59) and (21.4.60), we can write Xc (t) ¼ R(t) cos½F(t) ¼ Nc (t) þ a cos(c) Xs (t) ¼ R(t) sin½F(t) ¼ Ns (t) þ a sin(c)
(21:4:64)
To obtain the joint density of R(t) and F(t), we use the following transformation: xc ¼ r cos(f);
xs ¼ r sin(f)
(21:4:65)
The Jacobian of the transformation of Eq. (21.4.65) kJk ¼ r. In Eq. (21.4.63) we note that the term xc cos(c) þ xs sin(c) ¼ r cos(f) cos(c) þ r sin(f) sin(c) ¼ r cos(f c)
(21:4:66)
Substituting Eqs. (21.4.65) and (21.4.66) in Eq. (21.4.63), the joint pdf fRF(r, f) can be written as follows: r 0r ,1 (1=2sN2 ){r 2 þa2 2ar cos(fc)} fR, F (r, f) ¼ (21:4:67) e , 0 f 2p 2ps2N
21.4. Bandpass Stationary Random Processes
621
The marginal density of the envelope R(t) can be found by integrating Eq. (21.4.67) with respect to f. Substituting u ¼ f 2 c in Eq. (21.4.67), we obtain ð ar cosðuÞ r ½(r2 þa2 )=2s2N 1 2p r ½(r2 þa2 )=2s2N ar s2N e du ¼ 2 e I0 2 fR (r) ¼ 2 e (21:4:68) 2p 0 sN sN sN where we have utilized the fact that ear cosðuÞ is periodic with period 2p, that is, ear cosðuÞ ¼ ear cos(2pþu) . The integral in Eq. (21.4.68) is the zero-order modified Bessel function of the first kind given in Eq. (7.8.13) and shown in Fig. 7.8.4. ð ar 1 2p ½ar cos(u)=s2N e du (21:4:69) I0 2 ¼ 2p 0 sN Equation (21.4.68) can be normalized by substituting r a z¼ ; k¼ sN sN and written as 2
fZ (z) ¼ ze½(z þk
2
)=2
I0 (kz)
(21:4:70)
This is called Rice’s density and is shown in Fig. 7.8.5 and reproduced in Fig. 21.4.6 for k ¼ 0, . . . , 5. If a ¼ 0, the mean values are 0, and we obtain the Rayleigh density shown in Fig. 21.4.6. To obtain the phase density function, we integrate Eq. (21.4.67) over r, yielding ð 2p 1 2 2 2 fF (f) ¼ re(1=2sN ){r þa 2ar cos(fc)} dr (21:4:71) 2ps2N 0 Completing the squares in the expression r 2 þ a2 2ar cos(f c), we can rewrite Eq. (21.4.71) as follows: 2 ð 1 (1=2s2N )½1cos2 (fc) 1 r 1 r 2 a cos(f c) dr e fF (f) ¼ exp (21:4:72) 2p s 2 s s N N N 0
FIGURE 21.4.6
622
Random Processes and Linear Systems
Making the following substitutions for convenience in Eq. (21.4.72) z¼
r a ;k¼ ; u ¼ f c; b ¼ k cos(u) sN sN
as in the previous case, we can write fF (f) ¼ e
(1=2)k2 sin2 (u)
1 2p
ð1
2
ze(1=2)(zb) dz
(21:4:73)
0
Again substituting w ¼ z b in Eq. (21.4.73) and rearranging terms, we obtain ð 2 1 1 2 2 fF (f) ¼ e(1=2)k sin (u) (w þ b)e(1=2)w dw 2p b (21:4:74) ð ð1 b 1 1 (1=2)w2 (1=2)k2 sin2 (u) 1 (1=2)w2 we dw þ pffiffiffiffiffiffi pffiffiffiffiffiffi e dw fF (f) ¼ e 2p b 2p 2p b Evaluating the integrals, we obtain fF (f) ¼ e(1=2)k
2
sin2 (u)
1 (b2 =2) b e þ pffiffiffiffiffiffi F(b) 2p 2p
(21:4:75)
where F(b) is the Gaussian integral 1 F(b) ¼ pffiffiffiffiffiffi 2p
ð1
1 2 e(1=2)w dw ¼ pffiffiffiffiffiffi 2p b
ðb
2
e(1=2)w dw
1
Substituting back for b ¼ k cos(f 2 c) in Eq. (21.4.75), we have fF (f) ¼
1 (k2 =2) k cos (f c) (1=2)k2 sin2 (fc) pffiffiffiffiffiffi e þ F(b) e 2p 2p
(21:4:76)
which is the desired result for the phase density. When k ¼ 0, the phase f is uniformly distributed in (0, 2p). However, if k = 0, then the quadrature components are not independent, unlike the case when k ¼ 0. The phase density is shown in Fig. 21.4.7 for c ¼ p.
FIGURE 21.4.7
21.4. Bandpass Stationary Random Processes
623
FIGURE 21.4.8
Example 21.4.4
A received signal is of the form X(t) ¼ A cos½(vc þ vd )t þ N(t)
(21:4:77)
where N(t) is a bandpass white-noise signal with psd as shown in Fig. 21.4.8. We want to express N(t) in the form N(t) ¼ Nc0 (t) cos½(vc þ vd )t Ns0 sin½(vc þ vd )t
(21:4:78)
with the quadrature components of N(t) given by [Eq. (21.4.20a)] N(t) ¼ Nc (t) cos(vc t) Ns (t) sin(vc t)
(21:4:79)
We will express Nc0 (t) and Ns0 (t) in terms of Nc(t) and Ns(t). Expanding the righthand side (RHS) of Eq. (21.4.78), we obtain Nc0 (t) cos½(vc þ vd )t Ns0 (t) sin½(vc þ vd )t ¼ ½Nc0 (t) cos(vd t) Ns0 (t) sin(vd t) cos(vc t) ½Nc0 (t) sin(vd t) þ Ns0 (t) cos(vd t) sin(vc t)
(21:4:80)
Equating like coefficients in Eqs. (21.4.80) and (21.4.79), we obtain, in matrix form Nc (t) cos(vd t) sin(vd t) Nc0 (t) ¼ (21:4:81) Ns0 (t) Ns (t) sin(vd t) cos(vd t) The multiplying matrix in Eq. (21.4.81) is called a rotation matrix and is orthogonal. The inverse is the transpose, and hence we can write 0 Nc (t) cos(vd t) sin(vd t) Nc (t) (21:4:82) ¼ Ns0 (t) sin(vd t) cos(vd t) Ns (t) or Nc0 (t) ¼ Nc (t) cos(vd t) þ Ns (t) sin(vd t) Ns0 (t) ¼ Nc (t) sin(vd t) þ Ns (t) cos(vd t)
(21:4:83)
We can now find the autocorrelation functions, RNc0 (t) and RNs0 (t), in terms of RNc (t) and RNs (t) as follows: RNc0 (t) ¼ E½Nc0 (t)Nc0 (t þ t) ¼ E ½Nc (t) cos(vd t) þ Ns (t) sin(vd t)
½Nc (t þ t) cos½vd (t þ t) þ Ns (t þ t) sin½vd (t þ t) ¼ RNc (t) cos(vd t) cos½vd (t þ t) þ RNs (t) sin(vd t) sin½vd (t þ t) þ RNc Ns cos(vd t) sin½vd (t þ t) þ RNs Nc sin(vd t) cos½vd (t þ t)
(21:4:84)
624
Random Processes and Linear Systems
FIGURE 21.4.9
FIGURE 21.4.10
Substituting RNc ðtÞ ¼ RNs (t); RNc Ns (t) ¼ RNs Nc (t) from Eq. (21.4.26) in Eq. (21.4.84), we obtain RNc0 (t) ¼ RNc (t) cos(vd t) þ RNc Ns sin(vd t)
(21:4:85)
This equation [Eq. (21.4.85)] applies for any stationary random process N(t). For a whitenoise process, we have RNc Ns (t) ¼ 0,
t=0
(21:4:86)
and substituting Eq. (21.4.86) in Eq. (21.4.85), we obtain RNs0 (t) ¼ RNc (t) cos(vd t)
(21:4:87)
In a similar manner, we can obtain RNs0 (t) ¼ RNc (t) cos(vd t)
(21:4:88)
From Eqs. (21.4.87) and (21.4.88), we find that RNc0 (t) ¼ RNs0 (t). The psd SNc (v) is obtained from Eq. (21.4.47) as SNc (v) ¼ SNs (v) ¼ N0 ½u(v þ vB ) u(v vB )
(21:4:89)
and is shown in Fig. 21.4.9. The psd SNc0 (v) is obtained by using the modulation property of FT and is given by 1 SN (v) p½d(v þ vd ) þ d(v vd ) 2p c 1 ¼ ½SNc (v þ vd ) þ SNc (v vd ) 2
SNc0 (v) ¼
SNc0 (v) is shown in Fig. 21.4.10 for the two cases of vd . vB and vd , vB.
(21:4:90)
CHAPTER
22
Weiner and Kalman Filters
B 22.1
REVIEW OF ORTHOGONALITY PRINCIPLE
Random Variables
The estimate Y^ of a random variable Y by a function of another random variable g(X ) using the mean-square error criterion J ¼ E[Y 2 g(X )]2 is found in Chapter 18 as the conditional expectation given by Eq. (18.2.7): g(X) ¼ Y^ ¼ E½Y j X
(18:2:7)
and this estimator is also the orthogonal projection of Y in the space of X in the sense ^ ¼ E ½Y g(X)g(X) ¼ 0 E½1Y
(22:1:1)
In the case of linear estimation given by g(X) ¼ Y^ ¼ aT X
(22:1:2)
the coefficient vector a is determined from Eq. (18.2.23) as a ¼ R1 X RXY
(22:1:3)
where RX is the correlation matrix E[XX T] and RXY is the cross-correlation vector E[XY ]. We will now consider the one-dimensional case where Y^ ¼ aX. The orthogonality condition of Eq. (22.1.1) yields E ½Y aXaX ¼ E½XY aE½X 2 or
a¼
E½XY E½X 2
(22:1:4)
and using the orthogonality principle, the minimum mean-square error is Jmin ¼ E½12 ¼ E½1(Y aX) ¼ E½1Y ¼ E½Y 2
{E½XY}2 E½X 2
(22:1:5)
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
625
626
Weiner and Kalman Filters
and if X and Y have zero mean, then we can define rXY as the correlation coefficient between X and Y and write Eq. (22.1.5) as Jmin ¼ E½Y 2 ½1 r2XY
(22:1:6)
Random Processes
We can extend the preceding analysis to random processes. The input to the system shown in Fig. 22.1.1 is a zero mean random process X(t) consisting of a zero mean signal process S(t) and a zero mean additive noise N(t) that is independent of S(t), both defined in the estimation time interval T: X(t) ¼ S(t) þ N(t),
t [ T; E½S(t)N(t) ¼ E½S(t)E½N(t) ¼ 0,
t[T
(22:1:7)
The output of the system is Y(t). The system is observed during the observation time interval U. T and U may be different or the same. We want to design a linear system with impulse response h(t,u), u [ U such that the output Y(t) approximates the desired signal S(t) according to the minimum mean-square error criterion given by J ¼ min E ½S(t) Y(t)2 , h(t,u)
t [ T, u [ U
(22:1:8)
where ð h(t,u)X(u)du,
Y(t) ¼
t[T
(22:1:9)
U
For this case the orthogonality condition E{½S(t) Y(t)Y(t)} ¼ 0 becomes ð ð E S(t) h(t,u)X(u)du h(t,v)X(v)dv ¼ 0, t [ T U
(22:1:10)
U
This equation can be rearranged as follows: ð h(t,v) E½S(t)X(v) h(t,u)E½X(u)X(v)du dv,
ð U
t[T
(22:1:11)
U
If Eq. (22.1.11) has to be satisfied, then the quantity within braces must be equal to 0: ð hopt (t,u)E½X(u)X(v)du ¼ E½S(t)X(v), U
FIGURE 22.1.1
t [ T, v [ T
(22:1:12)
22.2 Wiener Filtering
We can now express Eq. (22.1.12) in terms of correlation functions as ð hopt (t,u)RX (u,v)du ¼ RXS (t,v), t [ T, v [ T
627
(21:1:13)
U
where hopt(t) is the optimum impulse response. The corresponding minimum mean-square error Jmin is given by Jmin ¼ E ½S(t) Yo (t)S(t) ð 2 ¼ E½S (t) hopt (t,u)E½X(u)S(t)du ð ¼ RS (t,t)
U
hopt (t,u)RXS (t,u)du,
t[T
(22:1:14)
U
If S(t) is replaced by S(t þ a) in Eqs. (22.1.12), then we have ð hopt (t,u)E½X(v)X(u)du ¼ E½X(v)S(t þ a), t [ T, v [ T
(22:1:15)
U
and Eq. (21.1.13) becomes ð hopt (t,u)RX (u,v)du ¼ RXS (t þ a,v),
t [ T, v [ T
(22:1:16)
U
In Eq. (22.1.16), if a ¼ 0, we have a filtering problem; if a . 0, we have a prediction or extrapolation problem; and if a , 0, we have a smoothing or interpolation problem. The corresponding equation for the minimum mean square error is ð Jmin ¼ E½S2 (t þ a) hopt (t,u)E½X(u)S(t þ a)du U
ð
hopt (t,u)RXS (t þ a,u)du,
¼ RS (t,t)
t[T
(22:1:17)
U
B 22.2
WIENER FILTERING
Noncausal Weiner Filtering (Unrealizable Filter)
We will now assume that the estimation time interval T and the observation time interval U are the real line, or, T ¼ U ¼ (21, 1). The processes S(t),N(t) are stationary, and the system is time-invariant but not causal. Under these assumptions Eq. (22.1.16) can be rewritten as ð1 hopt (t u)RX (u v)du ¼ RXS (t þ a v), t [ T, v [ T (22:2:1) 1
We make a change of variables t 2 u ¼ j and t 2 v ¼ t in Eq. (22.2.1) and we have ð1 hopt (j)RX (t j)dj ¼ RXS (t þ a) 1
Or hopt (t) RX (t) ¼ RXS (t þ a),
t[T
(22:2:2)
628
Weiner and Kalman Filters
The optimum transfer function Hopt(v) is obtained by taking FT of Eq. (22.2.2), yielding Hopt (v) ¼
SXS (v)e jva SX (v)
(22:2:3)
Since the signal and noise are assumed independent, the relevant correlation functions and the spectral densities are RX (t) ¼ RS (t) þ RN (t) RXS (t) ¼ E ½S(t) þ N(t)S(t þ t) ¼ RS (t) SX (v) ¼ SS (v) þ SN (v);
(22:2:4)
SXS (v) ¼ SS (v)
Substituting Eq. (22.2.4) into Eq. (22.2.3), we obtain Hopt (v) ¼
SS (v)e jva SS (v) þ SN (v)
(22:2:5)
If N(t) ¼ 0 in Eq. (22.1.7), then X(t) ¼ S(t), RX(t) ¼ RS(t), and Eq. (22.2.5) becomes Hopt (v) ¼ e jva
(22:2:6)
a pure prediction if a . 0 and a pure delay if a , 0. The minimum mean-square error is obtained from Eq. (22.1.17) as ð1 hopt (t u)RXS (t u þ a)du Jmin ¼ RS (0) 1
ð1
hopt (t)RXS (t þ a)dt
¼ RS (0)
(22:2:7)
1
and using Parseval’s theorem, we obtain the following in the frequency domain: ð 1 1 Jmin ¼ ½SS (v) SXS (v)Hopt (v)e jva dv (22:2:8) 2p 1 Substituting Eq. (22.2.3) in Eq. (22.2.8), we obtain ð 1 1 SXS (v) Jmin ¼ SS (v) SXS (v) dv 2p 1 SX (v) ð 1 1 SS (v)SX (v) jSXS (v)j2 dv ¼ 2p 1 SX (v) ð 1 1 SS (v)SX (v) jSXS (v)j2 ¼ dv; since SX (v) ¼ SX (v) 2p 1 SX (v) and if S(t) and N(t) are independent, Eq. (22.2.9) reduces to ð 1 1 SS (v)SN (v) Jmin ¼ dv 2p 1 SS (v) þ SN (v)
(22:2:9)
(22:2:10)
If the noise N(t) ¼ 0, then we obtain Jmin ¼ 0 from Eq. (22.2.10). It should be noted that the optimum transfer function Hopt(v) is noncausal and hence unrealizable. In the following examples we will assume that the signal S(t) and the noise N(t) are independent and both are zero mean stationary random processes.
22.2 Wiener Filtering
Example 22.2.1 to a system are
629
The autocorrelation functions of the input signal S(t) and the noise N(t) RS (t) ¼ ejtj=2
and RN (t) ¼ 4d(t)
We will estimate the signal S(t þ a) by finding the unrealizable filter Hopt(v). The power spectral densities (psd) of signal and noise are SS (v) ¼
4 1 þ 4v2
and
SN (v) ¼ 4
Since the signal and noise are independent, we can use Eq. (22.2.5) to obtain the optimum transfer function Hopt(v) as 4 e jva 1 þ 4v2 e jva ¼ Hopt (v) ¼ 4 2(1 þ 2v2 ) þ4 2 1 þ 4v The optimum transfer function jHopt(v)j is shown in Fig. 22.2.1. The corresponding optimum impulse response hopt(t) is obtained by taking the inverse Fourier transform (FT) of the optimum transfer function: pffiffi 1 hopt (t) ¼ pffiffiffi ejtaj= 2 4 2 hopt(t) is shown in Fig. 22.2.2 for values of the lag a ¼ 6,3,0,23. It is seen from the figure that hopt(t) is unrealizable because it is noncausal; however, it is approximately causal for lags a 6. It can be truly causal only for infinite lags. The minimum mean-square error is obtained from Eq. (22.2.10) as follows:
Jmin
1 ¼ 2p
4 ð 4 1 1 2 1 1 þ 4v2 dv ¼ pffiffiffi dv ¼ 2 4 2p 1 1 þ 2v 2 1 þ4 1 þ 4v2
ð1
FIGURE 22.2.1
630
Weiner and Kalman Filters
FIGURE 22.2.2
Example 22.2.2 This example will show that even for an infinite lag, hopt(t) cannot be causal. The autocorrelation functions of the input signal S(t) and the noise N(t) to a system are RS (t) ¼ ejtj=2
and
RN (t) ¼ e2jtj
We have to estimate the signal S(t þ a). We will determine the unrealizable optimum filter Hopt(v) and the corresponding minimum mean-square error. The psd’s of the signal and noise are SS (v) ¼
4 1 þ 4v2
and SN (v) ¼
4 4 þ v2
Since the signal and noise are independent, we can use Eq. (22.2.5) to obtain the transfer function Hopt(v) as follows: 4 e jva 2 4 þ v2 jva 1 þ 4v e Hopt (v) ¼ ¼ 4 4 5(1 þ v2 ) þ 1 þ 4v2 4 þ v2 The optimum transfer function is shown in Fig. 22.2.3.
FIGURE 22.2.3
22.2 Wiener Filtering
631
FIGURE 22.2.4
The transfer function Hopt(v) is not physically realizable since it violates the Paley – Wiener criterion given by Eq. (21.3.4): ð1 ln jH(v)j dv , 1 (21:3:4) 2 1 1 þ v Taking the inverse FT, we obtain the optimum impulse response hopt(t) as 1 3 hopt (t) ¼ d(t) þ ejtaj 5 10 hopt(t) is shown in Fig. 22.2.4 for values of a ¼ 6,3,0,23. The impulse response is also noncausal for any value of the lag a because of the presence of the impulse function. The minimum mean-square error is obtained from Eq. (22.2.9) and is given by
Jmin
1 ¼ 2p
4 4 ð1 4 2 1 þ 4v2 4 þ v2 dv ¼ 1 dv ¼ 2 4 4 2p 1 5(1 þ v ) 5 1 þ 1 þ 4v2 4 þ v2
ð1
Causal Weiner Filtering (Realizable Filter)
In the last section we derived an integral equation [Eq. (22.2.2)] for an unrealizable filter. We now impose the condition that the system is causal, that is, h(t) ¼ 0, t , 0. The estimation interval is the real line T ¼ f21 , t , 1g, and the observation interval is now U ¼ fu tg. The ranges for S(t) and N(t) are T. Under this condition, Eq. (22.1.13) becomes ðt hopt (t u)RX (u v)du ¼ RXS (t v), v [ U (22:2:11) 1
Making the change of variables t 2 u ¼ j and t 2 v ¼ t in Eq. (22.2.11), we obtain ð1 hopt (j)RX (t j)dj ¼ RXS (t), t 0 (22:2:12) 0
632
Weiner and Kalman Filters
Equation (22.2.12) is called the Wiener –Hopf integral equation. This equation is a necessary and sufficient condition for the causal optimum filter hopt(t) ¼ 0, t , 0 to exist. The corresponding mean-square error is obtained from Eq. (22.1.14) as ð1 Jmin ¼ RS (0) hopt (t)RXS (t)dt (22:2:13) 0
Even though Eq. (22.2.12) is similar to Eq. (22.2.2) with a ¼ 0, the solution is far more complex because h(t) was required to vanish along the negative time axis. If we take the FT of both sides of Eq. (22.1.12), we obtain ð1 ð 1 ð 1 RXS (t)ejvt dt ¼ hopt (j)RX (t j)dj ejvt dt 0
0
0
or ð1
RXS (t)ejvt dt ¼
0
ð 1 ð 1 0
e
hopt (j)ejvj RX (t j)dj
0 jv(tj)
dt
Substituting t 2 j ¼ b in Eq. (22.2.14), we obtain ð1 ð1 ð1 RXS (t)ejvt dt ¼ hopt (j)ejvj dj RX (b)ejvb db 0
0
(22:2:14)
(22:2:15)
j
The presence of j in the lower limit of the RHS of Eq. (22.2.15) poses a serious problem since it is a variable involved in the integration. We have to seek other methods for solving the Weiner – Hopf integral equation. We note that Eq. (22.2.11) or (22.2.12) is not dependent on the exact nature of the signal X(t) and S(t) but only on their autocorrelation functions, which are not unique. Thus, other signals that have the same autocorrelation functions will give the same result. Equation (22.2.12) will be solved by spectral factorization techniques for which we require some intermediate results. Whitening Filter
We are given an input stationary random process X(t) and want to find a causal impulse response g(t)u(t) such that the output process Z(t) given by ð1 Z(t) ¼ g(t)X(t t)dt (22:2:16) 0
is a white-noise process with autocorrelation RZ(t) ¼ d(t). We will make the assumption that the psd SX(v) of X(t) is a real rational function of v2 for real v so that all the poles and zeros of SX(v) in the complex v plane occur as complex conjugate pairs. From Eq. (21.2.9) we have SZ (v) ¼ 1 ¼ SX (v)jG(v)j2
(22:2:17)
Hence the required transfer function is given by 1 1 ¼ SX (v) G(v) G (v)
(22:2:18)
22.2 Wiener Filtering
633
where G(v) has poles in the top half and G (v) has poles in the bottom half of the v plane. The corresponding impulse responses g(t) and g 21(t), given by ð ð 1 1 1 1 1 e jvt dv g(t) ¼ G(v)e jvt dv; g1 (t) ¼ (22:2:19) 2p 1 2p 1 G(v) satisfy the causality and stability criteria: g(t) and g1 (t) ¼ 0, t , 0;
g(t) and g1 (t) ! 0 as t ! 1:
Under these conditions X(t) can be generated from the following equation: ð1 g1 (t)Z(t t)dt X(t) ¼
(22:2:20)
0
Spectral Factorization
As mentioned in the previous section, the psd SX(v) is a rational function of v2, and some properties are listed in Chapter 19, Eqs. (19.5.8)– (19.5.11). Additional properties relevant to Wiener filtering will be enumerated: 1. SX(v) is bounded because X(t) is assumed to be a zero mean stationary process: SX (v) , 1
(22:2:21)
2. As a consequence of property 1, poles of SX(v) cannot be real. 3. Since RX(t) is real, SX(v) ¼ SX (v) and hence it has complex conjugate poles and zeros (symmetry about the real axis). 4. From Eq. (19.5.8), SX(v) ¼ SX(2v) and hence it has symmetry about the imaginary axis. 5. As a consequence of properties 3 and 4, the poles and zeros have symmetry about both axes as shown in Fig. 22.2.5. Two methods of factoring SX(v) will be discussed. The first is called multiplicative factoring, given by SX (v) ¼ k
(v z1 )(v z2 ) (v zM ) (v z1 )(v z2 ) (v zM ) k (v p1 )(v p2 ) (v pN ) (v p1 )(v p2 ) (v pN )
FIGURE 22.2.5
(22:2:22)
634
Weiner and Kalman Filters
where pn, n ¼ 1, . . . , N are poles with positive imaginary parts and zn, n ¼ 1, . . . , M are zeros with positive imaginary parts. Thus, SX(v) can be represented by SX (v) ¼ ½SX (v)þ ½SX (v)
(22:2:23)
þ
where ½SX (v) has poles and zeros on the upper and ½SX (v) has poles and zeros in the lower half of the v plane as shown in Fig. 22.2.5. The second one is additive factoring, where we have a partial fraction expansion and separate the positive and negative roots in the top and bottom halves of the v plane. In this case SX(v) can be represented by SX (v) ¼ ½SX (v)þ þ ½SX (v)
(22:2:24)
In multiplicative factoring poles and zeros are involved, whereas in additive factoring only the poles are involved. We can now identify from Eq. (22.2.18) the transfer functions G(v) and G (v) as follows: G(v) ¼
Example 22.2.3
1 ; ½SX (v)þ
G (v) ¼
1 ½SX (v)
(22:2:25)
The psd SX(v) is given by SX (v) ¼ 25
(v2 þ 4) (v2 þ 9)(v2 þ 16)
The multiplicative factoring is obtained from the form of SX(v) and is given by ½SX (v)þ ¼ 5
(v þ 2j) ; (v þ 3j)(v þ 4j)
½SX (v) ¼ 5
(v 2j) (v 3j)(v 4j)
The additive factoring is obtained by obtaining a partial fraction expansion and expressing SX(v) as 25 12 5 SX (v) ¼ 7 v2 þ 16 v2 þ 9 25 3 5 3 5 þ ¼ 7 2(4 þ jv) 6(3 þ jv) 2(4 jv) 6(3 jv) and identifying 25 3 5 ; 7 2(4 þ jv) 6(3 þ jv) 25 3 5 ½SX (v) ¼ 7 2(4 jv) 6(3 jv) ½SX (v)þ ¼
The differences between these two factorings are clear.
Solution for Wiener– Hopf Filter Equation [Eq. (22.2.12)]
The method for finding the causal solution is to pass the input process X(t) through the causal whitening filter g(t) of Eq. (22.2.16), and then through another optimal causal filter gopt(t) so that these two filters are in tandem, will yield the desired hopt(t), which will yield
22.2 Wiener Filtering
635
the optimum Yo(t) as shown in Fig. 22.2.6: Yo (t) ¼ gopt (t) g(t) X(t)
(22:2:26)
From Eq. (22.2.26) the filter gopt(t) can be obtained as hopt (t) ¼ g(t) gopt (t) or gopt (t) ¼ hopt (t) g1 (t)
(22:2:27)
Taking FT on both sides of Eq. (22.2.27), we obtain Hopt (v) ¼ G(v)Gopt (v) or
Gopt (v) ¼
Hopt (v) G(v)
(22:2:28)
The filter gopt(t) is the optimal filter obtained by passing an input white noise to obtain the desired optimal response Yo(t). The Wiener –Hopf equation corresponding to gopt(t) is obtained from Eq. (22.2.2) with a ¼ 0: ð1 gopt (j)RZ (t j)dj ¼ RZS (t), t 0 (22:2:29) 0
In Eq. (22.2.29), we have RZ (t j) ¼ d(t j);
RZS (t) ¼ RXS (t) g(t)
(22:2:30)
Substituting Eq. (22.2.30) into Eq. (22.2.29) yields t0
gopt (t) ¼ RXS (t) g(t),
(22:2:31)
In other words, gopt(t) is the causal part of the noncausal term RXS(t) * g(2t). We can define the FT of RXS (t) g(t) in Eq. (22.2.31) as K(v). From the partial fraction expansion, K(v) can be additively factored by using Eq. (22.2.24) as K(v) ¼ Gopt (v) þ Gopt;L (v), where Gopt (v) has poles in the upper and Gopt;L (v) has poles in the lower half of the v plane, or K(v) ¼ SXS (v)G (v) ¼ Gopt (v) þ Gopt,L (v)
(22:2:32)
where SXS (v) Gopt (v) ¼ ½SXS (v)G (v)þ ¼ ½SX (v) þ SXS (v) Gopt,L (v) ¼ ½SXS (v)G (v)þ ¼ ½SX (v)
FIGURE 22.2.6
(22:2:33a) (22:2:33b)
636
Weiner and Kalman Filters
and IFT½K(v) ¼ k(t) ¼ gopt (t)u(t) þ gopt,L (t)u(t)
(22:2:34)
Substituting Eqs. (2.22.25) and (22.2.33a) in Eq. (22.2.28), we obtain the final form for the optimal causal transfer function Hopt(v) as 1 SXS (v) (22:2:35) Hopt (v) ¼ ½SX (v)þ ½SX (v) þ The minimum mean-square error associated with the causal optimal impulse response hopt(t) is given by Eq. (22.2.13): ð1 Jmin ¼ RS (0) hopt (t)RXS (t)dt (22:2:13) 0
The integral in the minimum mean-square error [Eq. (21.2.13)] can be written in the frequency domain using Parseval’s theorem: ð ð1 1 1 hopt (t)RXS (t)dt ¼ Hopt (v)SXS (v)dv (Parseval’s theorem) 2p 1 0 ð 1 1 Gopt (v)½G(v)SXS (v)dv ½Eq. (22:2:33) ¼ 2p 1 ð 1 1 SXS (v) Gopt (v) dv ¼ 2p 1 ½SX (v)þ ð 1 1 ¼ Gopt (v)Gopt (v)dv 2p 1 ð1 g2opt (t)dt (Parseval’s theorem) (22:2:36) ¼ 0
Substituting Eq. (22.2.36) in Eq. (22.2.13), the minimum mean-square error Jmin can be written in terms of the optimal filter gopt(t) for a white-noise input as ð1 Jmin ¼ RS (0) g2opt (t)dt (22:2:37) 0
If signal S(t) and noise N(t) are independent, then Eqs. (22.2.35) and (22.2.13) can be further simplified using Eq. (22.2.4). Equation (22.2.35) can be written as 1 SS (v) Hopt (v) ¼ ½SS (v) þ SN (v)þ ½SS (v) þ SN (v) þ (22:2:38) Gopt (v)
G(v)
Equation (22.2.13) can be written as ð1 Jmin ¼ RS (0)
hopt (t)RS (t)dt
(22:2:39)
0
Further simplifications are possible [21] if additionally N(t) is white noise with psd N0. Under this condition and from Eq. (22.2.4) we have SXS (v) ¼ SS (v) ¼ SX (v) N0
(22:2:40)
22.2 Wiener Filtering
637
Substituting Eq. (22.2.40) in filter equation (22.2.35), we can write 1 SX (v) N0 Hopt (v) ¼ ½SX (v)þ ½SX (v) ½SX (v) þ 1 ½SX (v)þ ½SX (v) ½N0 þ ½N0 ¼ ½SX (v) ½SX (v) þ ½SX (v)þ 1 ½N0 þ ½N0 þ ¼ ½S (v) X ½SX (v) þ ½SX (v)þ ½N0 þ N0 ¼1 (22:2:41) ½SS (v) þ N0 þ SS (v) þ N0 þ pffiffiffiffiffiffi The multiplicative factoring yields ½N0 þ ¼ N0 and the factor within the braces in Eq. (22.2.41) can be simplified as follows: N0 N0 N0 D(v) ¼ ¼ N(v) N(v) þ N0 D(v) SS (v) þ N0 þ N0 D(v)
(22:2:42)
In Eq. (22.4.42) the degree of N(v) is less than or equal to the degree of D(v), and hence N0 D(v) ¼ 1 þ ½ (A(v)) þ ¼ 1 (22:2:43) N(v) þ N0 D(v) þ Since all the poles of (A(v)) are in the lower half of the v plane, the factor ½(A(v)) þ ¼ 0 in Eq. (22.2.43). Substituting Eq. (22.2.43) in Eq. (22.2.41), we obtain a simplified expression for the optimal filter: pffiffiffiffiffiffi N0 Hopt (v) ¼ 1 (22:2:44) ½SS (v) þ N0 þ In a similar manner, Eq. (22.2.39) can also be simplified. Since RX(t) ¼ RS(t) þ N0d(t) the Wiener – Hopf equation (22.2.12) can be written as ð1 hopt (j)RS (t j)dj þ N0 hopt (t) ¼ RS (t), t 0 (22:2:45) 0
and Eq. (22.2.45) evaluated at t ¼ 0 becomes ð1 hopt (j)RS (j)dj ¼ RS (0) N0 hopt (0)
(22:2:46)
0
Substituting Eq. (22.2.46) in Eq. (22.2.39) we obtain Jmin ¼ N0 hopt (0)
(22:2:47)
Solution for Wiener– Hopf Prediction– Smoothing Equation
The form of the Wiener –Hopf (W – H) equation for prediction and smoothing is obtained by replacing RXS(t) with RXS(t þ a) in Eq. (22.2.12): ð1 hopt (j)RX (t j)dj ¼ RXS (t þ a), t 0 (22:2:48) 0
638
Weiner and Kalman Filters
As mentioned earlier, if a ¼ 0, we have a filtering problem whose solution is given by Eq. (22.2.35). If a . 0, we have a prediction problem, and if a , 0, we have a smoothing problem. Corresponding to the factor a, the optimal transfer function will be given by jva 1 e SXS (v) 1 Hopt (v) ¼ ¼ ½K(v)e jva þ (22:2:49) ½SX (v)þ ½SX (v) þ ½SX (v)þ and the minimum mean-square error by ð1 hopt (t)RXS (t þ a)dt
Jmin ¼ RS (0)
(22:2:50)
0
Similar to Eq. (22.2.34), we can define the following IFT jva e SXS (v) IFT ¼ IFT½e jva K(v) ½SX (v) ¼ k(t þ a) ¼ gopt (t þ a)u(t þ a) þ gopt, L (t a)u(t a) (22:2:51) and the minimum mean-square error Jmin will be given by an equation similar to Eq. (22.2.37) as ð1 ð1 Jmin ¼ RS (0) k2 (t þ a)dt ¼ RS (0) k2 (t)dt (22:2:52) 0
0
From Eq. (22.2.52) we can observe that the error is a maximum when the prediction time a ¼ 1, and a minimum for infinite lag time a ¼ 21. When a ¼ 0, we have Eq. (22.2.37). We will now determine a realizable filter for Examples 22.2.1 and 22.2.2 with the same assumption that S(t) and the noise N(t) are independent and both are zero mean stationary random processes. Example 22.2.4 We will consider Example 22.2.1, where the autocorrelation functions of the input signal S(t) and the noise N(t) to a system are RS (t) ¼ eðjtj=2Þ
and
RN (t) ¼ 4d(t)
The power spectral densities (psd’s) of signal and noise are SS (v) ¼
4 1 þ 4v2
and SN (v) ¼ 4
1. Filtering Problem: a ¼ 0. We will first estimate the signal S(t) by a realizable filter Hopt(v) using Eq. (22.2.38): pffiffiffi pffiffiffi 2 þ 2jv 2 2jv 8 þ 16v2 2 SS (v) þ SN (v) ¼ ¼2 2 1 þ 2jv 1 2jv 1 þ 4v pffiffiffi 21 1 1 þ 2jv 1 G(v) ¼ ¼ pffiffiffi þ ¼ pffiffiffi 2 2 2 2jv ½SS (v) þ SN (v) 2 2 þ 2jv pffiffiffi 21 1 1 2jv 1 ¼ pffiffiffi G (v) ¼ ¼ pffiffiffi ½SS (v) þ SN (v) 2 2 2jv) 2 2 2 2jv
22.2 Wiener Filtering
639
We find K(v) as follows: K(v) ¼
SS (v) ¼ SS (v)G (v) ½SS (v) þ SN (v)
1 2jv 4 2 ¼ pffiffiffi ¼ pffiffiffi 2 2 2 2jv 1 þ 4v 2 2jv (1 þ 2jv) Partial fraction expansion of K(v) yields
pffiffiffi
pffiffiffi 2 21 2 21 þ pffiffiffi K(v) ¼ (1 þ 2jv) 2 2jv
(A)
Taking the IFT of Eq. (A) above, we obtain k(t) ¼ gopt (t)u(t) þ gopt, L (t)u(t) ¼
i pffiffi h
pffiffiffi 2 1 e(t=2) u(t) þ e(t= 2) u(t)
(B)
We can identify Gopt(v) and Gopt,L(v) as follows:
pffiffiffi 2 21 SS (v) Gopt (v) ¼ ¼ ½K(v)þ ¼ (1 þ 2jv) ½SS (v) þ SN (v) þ
pffiffiffi 2 21 SS (v) p ffiffi ffi Gopt,L (v) ¼
¼ ½K(v) ¼ ½SS (v) þ SN (v) 2 2jv
Hence Hopt(v) is given by
pffiffiffi pffiffiffi 2 21 21 1 þ 2jv ¼ pffiffiffi Hopt (v) ¼ G(v)Gopt (v) ¼ pffiffiffi
1 þ 2jv 2 þ 2jv 2 2 þ 2jv This result pcan Eq. ffiffiffi also be obtained frompffiffiffiffiffi ffi (22.2.44) as follows. Substituting ½SS (v) þ N0 þ ¼ 2½ 2 þ 2jv =(1 þ 2jv) and N0 ¼ 2 in Eq. (22.2.44), we obtain pffiffiffi pffiffiffi 2 þ 2jv 1 2jv 21 2(1 þ 2jv) pffiffiffi ¼ pffiffiffi Hopt (v) ¼ 1 pffiffiffi ¼ 2 þ 2jv 2 þ 2jv 2( 2 þ 2jv which is the same as before. The inverse Fourier transforms of G(v), G (v), and Hopt(v) are given by pffiffiffi pffiffiffi 2 1 (t=pffiffi2) 2 1 ðt=pffiffi2Þ 1 1 e e g(t) ¼ d(t) u(t); g(t) ¼ d(t) u(t) 4 4 2 2 pffiffiffi 2 1 ðt=pffiffi2Þ e hopt (t) ¼ u(t) 2 The time function k(t) in Eq. (B), and g(t) and g(2t) corresponding to the poles and zeros of [SX (v)þ and ½SX (v) , and hopt(t), the optimum realizable impulse response, are shown in Fig. 22.2.7. To determine the minimum mean-square error Jmin, we need the function k 2(t), which is given by pffiffiffi 2
pffiffiffi 2 1 ½et u(t) þ e 2t u(t) k2 (t) ¼ (C)
640
Weiner and Kalman Filters
FIGURE 22.2.7
The minimum mean-square error is calculated by substituting the causal part of Eq. (C) in Eq. (22.2.52): ð1 ð1 pffiffiffi pffiffiffi pffiffiffi 2 2 Jmin ¼ RS (0) k (t)dt ¼ 1 ( 2 1) et dt ¼ 1 ( 2 1)2 ¼ 2( 2 1) 0
0
We can also calculate Jmin using Eqs. (22.2.39) or (22.2.47): ð1 ð1 pffiffi 1 pffiffiffi e(t= 2) e(t=2) dt Jmin ¼ RS (0) hopt (t)RS (t)dt ¼ 1 8 0 0 2þ h pffiffiffi i1 pffiffi pffiffiffi ¼ 1 (2 2 3)e(t= 2) e(t=2) ¼ 2( 2 1) 0
pffiffiffi 4 2 pffiffiffi ¼ pffiffiffi ¼ 2( 2 1) ¼ N0 hopt (0) ¼ 2þ 8 1þ 2 pffiffiffi All three equations yield the same result: Jmin ¼ 2( 2 1) ¼ 0:8284. The causal part of the noncausal solution of Example 22.2.1 given by hopt pffiffi pffiffi noncausal(t) ¼ 82 e(t= 2) u(t) and the causal solution of this example, hoptcausal (t) ¼ pffiffi pffiffiffi ½( 2 1=2)e(1= 2) u(t), are shown in Fig. 22.2.8. The minimum mean-square errors in
FIGURE 22.2.8
22.2 Wiener Filtering
641
both examples are given by Jmin noncausal ¼ 0.7071 and Jmin causal ¼ 0.8284. The differences between the causal and noncausal solutions are not that significant for this problem, but the complexity of getting a causal solution is much greater. From K(v) in Eq. (A) for a delay a, we can write pffiffiffi pffiffiffi 2( 2 1) 2( 2 1) jva þ pffiffiffi K(v)e jva ¼ e (1 þ 2jv) ( 2 2jv) i pffiffi h
pffiffiffi 2 1 e½(tþa)=2 u(t þ a) þ e½(tþa)= 2 u(t a) () and the time function k(t þ a) is shown for both a . 0 and a , 0 in Fig. 22.2.9. 2. Prediction Problem: a . 0. For the prediction problem of a . 0, the causal part of k(t þ a) from Fig. 22.2.9 is given by
pffiffiffi 2 1 e½(tþa)=2 u(t), a . 0 k(t þ a)casual ¼ and the FT of this equation yields,
pffiffiffi
pffiffiffi 2 1 e(a=2) 2 2 1 e(a=2) jva ½K(v)e þ ¼ ¼ 1 1 þ 2jv 2 þ jv and substituting the preceding equation in Eq. (22.2.49), we obtain the optimum transfer function for the prediction problem pffiffiffi 1 þ 2jv 2( 2 1Þe(a=2) jva Hopt (v) ¼ G(v)½K(v)e þ ¼ pffiffiffi 1 þ 2jv 2( 2 þ 2jv) pffiffiffi (a=2) ( 2 1)e ¼ pffiffiffi 2 þ 2jv and the optimum impulse response given by pffiffiffi 2 1 ½(1=pffiffi2)½tþ(a=2) e hopt (t) ¼ u(t), 2
FIGURE 22.2.9
a.0
642
Weiner and Kalman Filters
Since a . 0, the minimum mean-square error is obtained by substituting the causal part of pffiffiffi Eq. (C), k2 (t) ¼ ( 2 1)2 et u(t), in Eq. (22.2.52) to obtain ð1 ð 1 pffiffiffi pffiffiffi Jmin ¼ RS (0) k2 (t)dt ¼ 1 ( 2 1)2 et dt ¼ 1 ( 2 1)2 ea , a . 0 a
a
3. Smoothing Problem: a , 0. For the smoothing problem of a , 0, the causal part of k(t þ a) from Fig. 22.2.9 is h i pffiffi pffiffiffi k(t þ a)causal ¼ ( 2 1) e½(tþa)=2 u(t þ a) þ e½(tþa)= 2 ½u(t a) u(t) , a , 0 and the FT of this equation yields ½K(v)e
jva
pffiffi # e jva e jva ea= 2 þ pffiffiffi 1 þ 2jv 2 2jv " pffiffiffi pffiffi # pffiffiffi ( 2 þ 1)e jva (1 þ 2jv)ea= 2 pffiffiffi ¼ 2( 2 1) (1 þ 2jv)( 2 2jv)
pffiffiffi þ ¼ 2( 2 1)
"
and substituting this equation in Eq. (22.2.49), we obtain the optimum transfer function for the smoothing problem: " pffiffiffi pffiffi # pffiffiffi 1 þ 2jv ( 2 þ 1)e jva (1 þ 2jv)ea= 2 pffiffiffi 2( 2 1) Hopt (v) ¼ pffiffiffi 2( 2 þ 2jvÞ (1 þ 2jv)( 2 2jvÞ " pffiffiffi pffiffi # pffiffiffi ( 2 þ 1)e jva (1 þ 2jv)ea= 2 ¼ ( 2 1) (2 þ 4v2 ) To obtain the minimum mean-square error, we note that since a , 0 we have to use the entire Eq. (C) in Eq. (22.2.52) and write ð 0 pffiffi ð1 ð1 pffiffiffi k2 (t)dt ¼ 1 ( 2 1)2 e 2t dt þ et dt Jmin ¼ Rs (0) a
pffiffi pffiffiffi 2 1 2a ¼ 1 ( 2 1) pffiffiffi (1 e ) þ 1 , 2
a
0
a,0
The graph of the minimum mean-square error Jmin(a) for values of a ranging from 21 to 1 is shown in Fig. 22.2.10. The MSE Jmin reaches a minimum value for infinite lag, and this value is given by pffiffi pffiffiffi 2 1 2a p ffiffi ffi Jmin (1) ¼ 1 ( 2 1) (1 e ) þ 1 2 a!1 pffiffiffi pffiffiffi 2þ1 1 ¼ pffiffiffi ¼ 1 ( 2 1)2 pffiffiffi 2 2 and the maximum error for infinite prediction time is pffiffiffi Jmin (1) ¼ 1 ½( 2 1)2 ea
a!1
¼1
Example 22.2.5 This example will find a causal filter for Example 22.2.2, where the autocorrelation functions of the input signal S(t) and the independent noise N(t) to a
22.2 Wiener Filtering
643
FIGURE 22.2.10
system are given by RS (t) ¼ eðjtj=2Þ
and
RN (t) ¼ e2jtj
The power spectral densities of signal and noise are SS (v) ¼
4 1 þ 4v2
and
SN (v) ¼
4 4 þ v2
1. Filtering Problem: a . 0. From Example 22.2.4 we can write 1 þ v2 (1 þ 4v2 )(4 þ v2 ) pffiffiffi pffiffiffi 2 5(1 þ jv) 2 5(1 jv) ¼ (1 þ 2jv)(2 þ jv) (1 2jv)(2 jv)
SX (v) ¼ SS (v) þ SN (v) ¼ 20
G(v) ¼
(1 þ 2jv)(2 þ jv) (1 2jv)(2 jv) pffiffiffi pffiffiffi ; G (v) ¼ 2 5(1 þ jv) 2 5(1 jv)
We find K(v) as follows: K(v) ¼ SS (v)G (v) ¼
4 (1 2jv)(2 jv) 2(2 jv) pffiffiffi ¼ pffiffiffi 1 þ 4v2 2 5(1 jv) 5(1 þ 2jv)(1 jv)
Partial fraction expansion of K(v) yields pffiffiffi 2 5 1 1 þ K(v) ¼ 3 (1 þ 2jv) 5(1 jv)
(A)
Taking the IFT of Eq. (A), we obtain pffiffiffi 5 (t=2) 2 t e u(t) þ e u(t) k(t) ¼ gopt (t)u(t) þ gopt,L (t)u(t) ¼ 3 5 k(t) is shown in Fig. 22.2.11.
(B)
644
Weiner and Kalman Filters
FIGURE 22.2.11
We can identify Gopt(v) and Gopt,L(v) as pffiffiffi SS (v) 2 5 Gopt (v) ¼ ¼ ½K(v)þ ¼ 3(1 þ 2jv) ½SS (v) þ SN (v) þ pffiffiffi SS (v) 2 5 Gopt;L (v) ¼ ¼ ½K(v) ¼ 15(1 jv) ½SS (v) þ SN (v)
Hence Hopt(v) is given by pffiffiffi (1 þ 2jv)(2 þ jv) 2 5 (2 þ jv) pffiffiffi ¼ Hopt (v) ¼ G(v)Gopt (v) ¼ 3(1 þ 2jv) 3(1 þ jv) 2 5(1 þ jv) The inverse Fourier transforms of G(v), G (v), and Hopt(v) are given by pffiffiffi 5 t e u(t) 3d(t) þ 2d0 (t); g(t) ¼ 10 1 1 hopt (t) ¼ et u(t) þ d(t) 3 3
pffiffiffi 5 t e u(t) 3d(t) 2d0 (t) g(t) ¼ 10
The optimum impulse response may not be realizable because of the presence of the impulse function. To determine the minimum mean-square error Jmin, we need the function k 2(t), which is given by k2 (t) ¼
5 t 4 e u(t) þ e2t u(t) 9 25
(C)
The minimum mean-square error is calculated by substituting the causal part of Eq. (C) in Eq. (22.2.52): ð1 Jmin ¼ RS (0) 0
k2 (t)dt ¼ 1
5 9
ð1 0
et dt ¼
4 9
22.2 Wiener Filtering
645
We can also calculate Jmin using Eqs. (22.2.39) or (22.2.47): ð1 Jmin ¼ RS (0) 0
ð1
1 t 1 e þ d(t) eðt=2Þ dt hopt (t)RS (t)dt ¼ 1 3 0 3 1 2 5 4 þ ¼1 ¼1 ¼ 3 9 9 9
The two equations yield the same result Jmin ¼ 49 ¼ 0:4444. The causal part of the noncausal solution of Example 22.2.2 for a ¼ 0 is given by 3 2t hopt noncausal (t) ¼ 15d(t) þ 10 e u(t), and the causal solution of this example is hoptcausal 2t (t) ¼ 13d(t) þ 13e u(t), shown in Fig. 22.2.12. The minimum mean-square errors in both examples are given by Jminnoncausal ¼ 0.4 and Jmincausal ¼ 0.4444. The differences between the causal and noncausal solutions in this example also are not that significant for this problem, but the complexity of getting a causal solution is much greater. From K(v) in Eq. (A) for a delay a, we can write K(v)e
jva
pffiffiffi 2 5 1 1 þ ¼ e jva 3 (1 þ 2jv) 5(1 jv) pffiffiffi 5 ½(tþa)=2 2 ½tþa e , u(t þ a) þ e u(t a) 3 5
2. Prediction Problem: a . 0. For the prediction problem of a . 0, the causal part of k(t þ a) is given by k(t þ a)causal
pffiffiffi 5 ½(tþa)=2 e ¼ u(t), 3
a.0
and the FT of the preceding equation yields ½K(v)e
jva)
pffiffiffi (a=2) pffiffiffi 5e 2 5e(a=2) ¼ þ ¼ 1 3(1 þ 2jv) 3 2 þ jv
FIGURE 22.2.12
646
Weiner and Kalman Filters
and substituting the preceding equation in Eq. (22.2.49), we obtain the optimum transfer function for the prediction problem
Hopt (v) ¼ G(v)½K(v)e ¼
jva
pffiffiffi (1 þ 2jv)(2 þ jv) 2 5e(a=2) pffiffiffi þ ¼ 3(1 þ 2jv) 2 5(1 þ jv)
(2 þ jv) (a=2) e 3(1 þ jv)
and the impulse response, given by 1 hopt (t) ¼ e½tþ(a=2) u(t) þ d(t), 3
a.0
Since a . 0, the minimum mean-square error is obtained by substituting the causal part of Eq. (C), k2 (t) ¼ 59 et u(t), in Eq. (22.2.52) to obtain ð1 Jmin ¼ RS (0)
2
ð1
k (t)dt ¼ 1 a
a
5 t 5 e dt ¼ 1 ea , 9 9
a.0
3. Smoothing Problem: a , 0. For the smoothing problem of a , 0, the causal part of k(t þ a) is
k(t þ a)causal
pffiffiffi 5 ½(tþa)=2 2 e ¼ u(t þ a) þ etþa ½u(t a) u(t) , 3 5
a,0
and the FT of the equation above yields
½K(v)e
jva
pffiffiffi 2 5 e jva 1 e jva ea þ þ ¼ 3 1 þ 2jv 5 (1 jv) pffiffiffi 2 5 3e jva (2 jv) (1 þ 2jv)ea ¼ 3 5(1 þ 2jv)(1 jv)
and substituting the preceding equation in Eq. (22.2.49), we obtain the optimum transfer function for the smoothing problem: pffiffiffi (1 þ 2jv)(2 þ jv) 2 5 3e jva (2 jv) (1 þ 2jv)ea pffiffiffi Hopt (v) ¼ 3 5(1 þ 2jv)(1 jv) 2 5(1 þ jv) jva 1 3e (4 þ v2 ) (2 þ jv)(1 þ 2jv)ea ¼ 3 5(1 þ v2 )
22.3 Discrete Kalman Filter
647
FIGURE 22.2.13
For obtaining the minimum mean-square error, we note that since a , 0, we have to use the entire Eq. (C) in Eq. (22.2.52) and write ð ð1 ða 5 4 0 2t k2 (t)dt ¼ 1 e dt þ et dt Jmin ¼ RS (0) 9 25 a a 0 5 2 4 2 ¼1 (1 e2a Þ þ 1 ¼ (1 e2a ), a , 0 9 25 9 45 The graph of the minimum mean-square error Jmin (a) for values of a ranging from 21 to 1 is shown in Fig. 22.2.13. Jmin reaches a minimum value for infinite lag, and this value is given by 4 2 4 2 2 2a ¼ ¼ Jmin (1) ¼ ½(1 e ) 9 45 9 49 5 a!1 and the maximum error for infinite prediction time is 5 Jmin (1) ¼ 1 ea ¼1 9 a!1
B 22.3
DISCRETE KALMAN FILTER
The Wiener filter discussed in the previous sections was derived under the imposition of the physical realizability condition on the filter. As a consequence of this condition, Eq. (22.2.12) is not a convolution, and transform techniques could not be directly used, so we had to resort to spectral factorization. Further, the input processes are assumed to be stationary, and the system is linear time-invariant. To circumvent these and other difficulties, Kalman and Bucy [27] proposed a recursive linear digital filter that accommodates nonstationary processes. We will motivate the concept of recursive estimation. Sections 22.3 and 22.4 are adapted with permission from Chapter 9 of the book, Nonlinear FItering and Smoothing, by Venkatarama Krishnan, originally published by John Wiley and Sons in 1984, and republished by Dover Publications in 2005.
648
Weiner and Kalman Filters
Recursive Estimation
We will assume that fXi, i ¼ 1,2, . . . , kg is a discrete signal random process and that fYi, i ¼ 1,2, . . . , kg is an observation or measurement random process related to the signal process by the equation i ¼ 1, 2, . . . , k
Y i ¼ X i þ Vi ,
(22:3:1)
where fVi, i ¼ 1,2, . . . , kg is a zero mean white-noise sequence with E½Vi Vj ¼ s2V dij , i, j ¼ 1,2, . . . , k. We will further assume that Xi ¼ X for all i. The schematic is shown in Fig. 22.3.1. We want to estimate the process fXi ¼ Xg given the observation process fYi, i ¼ 1, ^ the con2, . . . , kg. From Eq. (18.3.10) the minimum variance unbiased estimator is X, ditional expectation of X given fYi, i ¼ 1,2, . . . , kg, which can be written as 1 X^ k ¼ k
k X
Yi
(22:3:2)
i¼1
An additional observation Ykþ1 is available at time k þ 1. The new estimate is given by X^ kþ1 ¼
kþ1 1 X Yi k þ 1 i¼1
(22:3:3)
However, the estimate given by Eq. (22.3.3) processes all observations fYi, i ¼ 1, . . . , k þ 1g again, and this is unacceptable for machine computation. We want to use the prior estimate X^ from Eq. (22.3.2) to save on computation time. Hence we can rewrite Eq. (22.3.3) as follows: kþ1 k 1X Ykþ1 k ^ 1 Xk þ Ykþ1 ¼ Yi þ k þ 1 k i¼1 kþ1 kþ1 kþ1 1 1 1 Ykþ1 ¼ X^ k þ (Ykþ1 X^ k ) ¼ 1 X^ k þ kþ1 kþ1 kþ1
X^ kþ1 ¼
(22:3:4)
In Eq. (22.3.4) the term (Ykþ1 X^ k ) is the innovations process, so called because it contains new information. We have utilized the past estimate to update the present estimate, thus achieving recursive action. More generally, the first equation in Eq. (22.3.4) can be expressed in the form X^ kþ1 ¼ Kk0 X^ k þ Kk Ykþ1
(22:3:5)
where the weighting coefficients Kk0 and Kk have to be determined using the optimality criteria of unbiasedness and minimum variance. We will reformulate this problem so that the solution in the general case will be clear. The time just before the observation
FIGURE 22.3.1
22.3 Discrete Kalman Filter
649
FIGURE 22.3.2
Ykþ1 is denoted by (k þ 1)2, and the time just after the observation is denoted by (k þ 1)þ. Hence X^ kþ1 () is the estimate just before the observation, and X^ kþ1 (þ) is the estimate just after the observation. The timing diagram of Fig. 22.3.2 illustrates these quantities. The evolution of the state between the times kþ and (k þ 1)2 is governed by the dynamics of the system, and in this case, since Xk is a constant X for all times, we have X^ k (þ) ¼ X^ kþ1 ()
Xk (þ) ¼ X and Xkþ1 () ¼ X and hence
(22:3:6)
Thus there are two distinct zones: (1) between observations, where the estimate is governed by the dynamics of the system; and (2) across observations, where the estimate is updated by the new observation. The recursive formulation of Eq. (22.3.4) can be written in two stages as X^ kþ1 () ¼ X^ k (þ)
Between observations: Across observations:
X^ kþ1 (þ) ¼ X^ kþ1 () þ
1 ½Ykþ1 X^ kþ1 () kþ1 Innovations
(22:3:7)
The two equations in Eq. (22.3.7) can be combined into a single equation as X^ kþ1 (þ) ¼ X^ k (þ) þ
1 ½Ykþ1 X^ k (þ) kþ1
(22:3:8)
which is Eq. (22.3.4) in a more precise form. Formulation of Discrete Kalman Filter
We can now formulate the recursive discrete nonstationary filtering problem. Boldface will represent both matrices and vectors. A discrete linear nonstationary signal process is represented by the vector difference equation Signal process:
Xk ¼ Fk, k1 Xk1 þ Gk1 Wk1 ,
k [ {1, 2, . . . , N}, X0
(22:3:9)
The observation process is another discrete linear process represented by Observation process:
Yk ¼ Hk Xk þ Vk ,
k [ {1, 2, . . . , N}
(22:3:10)
^ k for the Given the observations fYk, k [ f1, 2, . . . , Ng, we have to find the estimate X signal Xk under the optimality criteria of unbiasedness and minimum variance, and this ^ k ¼ E½X j Yk , k [ {1, 2, . . . , N}. estimate is given by the conditional expectation X
650
Weiner and Kalman Filters
The following assumptions are made regarding Eqs. (22.3.9) and (22.3.10). Assumptions 1. The signal process fXk, k ¼ [1, 2, . . ., N ]g with initial condition X0 is an n-vector, and the observation process fYk, k ¼ [1, 2, . . ., N ]g with initial condition Y0 ¼ 0 is an m-vector at time t ¼ tk. 2. Fk,k21 is an n n nonsingular discrete signal transition matrix from time t ¼ tk21 to time t ¼ tk. 3. Gk is an n r discrete nonrandom rectangular matrix at time t ¼ tk. 4. Wk is an r-vector of zero mean white Gaussian signal noise sequence with covariance matrix E½Wk WTk ¼ Qk . 5. Vk is an m-vector of zero mean white Gaussian observation noise sequence with covariance matrix E½Vk VTk ¼ Rk where Wk and Vk are independent, E½Wk VTk ¼ 0. All quantities are evaluated at time t ¼ tk. 6. Hk is an m n discrete nonrandom rectangular weighting matrix at time t ¼ tk. 7. The Gaussian initial condition vector X0 is assumed independent of both the noise vector sequences fWkg and fVkg. ^ k is the expectation of Uk 8. If Uk is any random vector sequence, then the estimate U conditioned on the observations fYk, k ¼ [1, 2, . . . , N ]g and will be represented by ^ k ¼ E½Uk j Yk , k ¼ 1, 2, . . . , N. Ek ½Uk ¼ U ^ k Xk ) and the noise vector 9. The estimation error vector sequence Xk ¼ (X sequences fWkg and fVkg are independent. 10. The innovations sequence fnk, k ¼ [1,2, . . . , N ]g is also an m-vector, given by ^ k, nk ¼ Yk Hk X
k ¼ 1, 2, . . . , N
(22:3:11)
^ k Xk ½X ^ k Xk T is 11. The n n error covariance matrix defined by, Pk ¼ E½X positive definite. Under the these assumptions, the discrete Kalman filter takes the form as shown: Across Observations. At the observation time tk, the conditional expectations ^ k (þ) and Ek ½Xk ^ k () satisfy Ek ½Xk tk (þ) ¼ X ¼ X tk () ^ k (), ^ k () þ Kk ½Yk Hk X ^ k (þ) ¼ X X ^ 0 ¼ X0 k [ {1, 2, . . . , N}, X
(22:3:12a)
and the covariance matrix Pk(þ) satisfies Pk (þ) ¼ (I Kk Hk )Pk (),
k [ {1, 2, . . . , N}
(22:3:12b)
where the n m matrix Kk, called the Kalman gain, is given by Kk ¼ Pk ()HTk ½Hk Pk ()HTk þ Rk 1 ,
k [ {1, 2, . . . , N}
(22:3:13)
Between Observations. Between observation times tk21 and tk the estimates satisfy ^ k1 (þ), ^ k () ¼ Fk, k1 X X
k [ {1, 2, . . . , N}
(22:3:14a)
22.3 Discrete Kalman Filter
651
and the covariance matrix satisfies Pk () ¼ Fk, k1 Pk1 (þ)FTk, k1 þ Gk1 Qk1 GTk1 , k [ {1, 2, . . . , N}
(22:3:14b)
^ k () be the We can gain insight into the problem by deriving these equations. Let X estimate determined at time tk(2) and Yk, the observation at time tk. We will use the prior ^ k () and the observation Yk to get a new estimate to X ^ k (þ) at time tk(þ). We estimate X can write an equation analogous to the scalar Eq. (22.3.5) as ^ k (þ) ¼ K0 X ^ X k k () þ Kk Yk
(22:3:15)
where the weighting n n matrix Kk0 and weighting n m matrix Kk have to be determined using the two criteria of unbiasedness and minimum variance. If Xk is the true unobservable value of the state at time tk, we can define estimation ˜ k (þ) at time tk(þ) and X ˜ k () at time tk () as errors X ^ k (þ) Xk ˜ k (þ) ¼ X X ˜ k () ¼ X ^ k () Xk X
(22:3:16)
We can convert Eq. (22.3.15) into an error equation by subtracting Xk from both sides: ^ k () Xk þ K0 Xk Xk þ Kk Yk ^ k (þ) Xk ¼ K0 ½X X k k
(22:3:17)
Substituting for Yk from Eq. (22.3.10) and error matrices from Eq. (22.3.16) in Eq. (22.3.17), we obtain 0 ˜ ˜ k (þ) ¼ K0 X X k k () þ ½Kk þ Kk Hk I Xk þ Kk Vk
(22:3:18)
Taking expected value of both sides of Eq. (22.3.18), we obtain ˜ k () þ ½K0 þ Kk Hk IE½Xk þ Kk E½Vk ˜ k (þ) ¼ K0 E½X E½X k k
(22:3:19)
^ k is to be unbiased, then we must have If the estimator X ˜ k () ˜ k (þ) ¼ 0 ¼ E½X E½X
(22:3:20)
Hence, with E[Vk] ¼ 0 and E[Xk] = 0 in Eq. (22.3.19), we deduce K0k þ Kk Hk I ¼ 0
or K0k ¼ I Kk Hk
(22:3:21)
We have thus expressed the matrix K0k in terms of the matrix Kk. Across Observations. Substituting Eq. (22.3.21) in Eq. (22.3.15), we establish the signal update equation: ^ k (þ) ¼ X ^ k () þ Kk ½Yk Hk X ^ k (), X
^0 ¼X ^ 0 (22:3:22) k [ {1, 2, . . . , N}, X
Thus yet unknown matrix Kk is determined using the minimum variance criterion. The estimation error equation [Eq. (22.3.18)] can be rewritten by substituting K0k as ˜ k () þ Kk Vk ˜ k (þ) ¼ ½I Kk Hk X X ˜ k () þ Kk ½Vk Hk X ˜ k () ¼X
(22:3:23)
652
Weiner and Kalman Filters
Equation (22.3.12a) represents the estimate update, whereas Eq. (22.3.23) represents the estimate error update. We can now define the error covariance matrices Pk(þ) and Pk(2) as ˜ k (þ)X ˜ T (þ); Pk (þ) ¼ E½X k
˜ k ()X ˜ T () Pk () ¼ E½X k
(22:3:24)
Substituting Eq. (22.3.23) in Eq. (22.3.24), we obtain ˜ k () ˜ k () þ Kk ½Vk Hk X Pk ðþÞ ¼ E X ˜ k ())T ˜ k () þ Kk ½Vk Hk X (X
¼ (I Kk Hk )Pk ()(I Kk Hk )T þ Kk Rk KTk
(22:3:25)
The variance function that should be minimized is T
˜ k (þ) ¼ Tr Pk (þ) ˜ (þ)X E½X k
(22:3:26)
where Tr Pk (þ) is the trace of the covariance matrix Pk (þ). We can minimize Eq. (22.3.26) with respect to the matrix Kk by solving
@ @ Tr Pk (þ) ¼ Tr (I Kk Hk )Pk ()(I Kk Hk )T þ Kk Rk KTk @Kk @Kk ¼0 (22:3:27) Equation (22.3.27) is solved by using the result from Eq. (16.3.23) stating that if a matrix B is symmetric and A is any other matrix, then @ Tr½ABAT ¼ 2AB @A
(16:3:23)
Applying Eq. (16.3.23) to Eq. (22.3.27) results in @ Tr Pk (þ) ¼ 2(I Kk Hk )Pk ()HTk þ Kk Rk ¼ 0 @Kk
(22:3:29)
Solving for Kk in Eq. (22.3.29), we obtain the equation for the Kalman gain matrix Kk ¼ Pk ()HTk ½Hk Pk ()HTk þ Rk 1 ,
k [ {1, 2, . . . , N}
(22:3:30)
which is Eq. (22.3.13). Substituting Kk from Eq. (22.3.30), in Eq. (22.3.25), we obtain Eq. (22.3.12b): Pk (þ) ¼ (I Kk Hk )Pk (),
k [ {1, 2, . . . , N}
(22:3:31)
We have shown the result for “across observations”. Between Observations. The conditional expectation of the signal process shown below Xk ¼ Fk, k1 Xk1 þ Gk1 Wk1 ;
k [ {1, 2, . . . , N}, X0
(22:3:32)
conditioned on the observations fYk21g, is given by ^ k1 (þ) þ Gk1 W ^ k1 , ^ k () ¼ Fk, k1 X X
k [ {1, 2, . . . , N}
(22:3:33)
^ k () ¼ E½Xk j {Yk1 }. Since Wk21 is independent of the observations where X ^ k1 ¼ E½Wk1 j {Yk1 } ¼ E½Wk1 ¼ 0, and Eq. (22.3.33) reduces to fYk21g, W Eq. (22.3.14a): ^ k1 (þ), ^ k () ¼ Fk, k1 X X
k [ {1, 2, . . . , N}
(22:3:34)
22.3 Discrete Kalman Filter
653
FIGURE 22.3.3
To demonstrate Eq. (22.3.14b) we subtract Eq. (22.3.32) from Eq. (22.3.34) to obtain the error equation ˜ k () ¼ Fk, k1 X ˜ k1 (þ) Gk1 Wk1 X
(22:3:35)
and form the error covariance matrix Pk () ¼ E½Xk ()X()Tk , given by ˜ k1 (þ) Gk1 Wk1 ½Fk, k1 X ˜ k1 (þ) Gk1 Wk1 T Pk () ¼ E ½Fk, k1 X T T ˜ T (þ)FT ˜ k1 (þ)X ¼ E Fk, k1 X k, k1 þ Gk1 Wk1 Wk1 Gk1 k1 ˜ k1 (þ)WT GT Gk1 Wk1 X ˜ T (þ)FT Fk, k1 X (22:3:36) k1 k1 k1 k, k1 From assumption 9 (above) that the estimation errors are independent of the noise sequences, Eq. (22.3.36) can be simplified to Eq. (22.3.14b): Pk () ¼ Fk, k1 Pk1 (þ)FTk, k1 þ Gk1 Qk1 GTk1 ,
k [ {1, 2, . . . , N}
(22:3:37)
It is to be particularly noted that the variance update equation [Eq. (22.3.31)] does not depend on either the estimate or the observations, whereas the signal update [Eq. (22.3.22)] is dependent on the covariance matrix via the Kalman gain. The timing diagram similar to Fig. 22.3.2 for the realization of the Kalman filter is shown in Fig. 22.3.3. The block diagram for the implementation of the discrete Kalman filter is shown in Fig. 22.3.4.
FIGURE 22.3.4
654
Weiner and Kalman Filters
Comments 1. The equations for across and between measurements can be combined into a single equation for both the estimated mean and the unconditional variance, and written as follows: ^ k () ¼ Fk, k1 X ^ k1 () , ^ k1 () þ Kk1 ½Yk1 Hk1 X X k [ {1, 2, . . . , N}
(22:3:38)
Pk () ¼ Fk, k1 ½(I Kk1 Hk1 )Pk1 ()FTk, k1 þ Gk1 Qk1 GTk1 ,
k [ {1, 2, . . . , N}
(22:3:39)
where, from Eq. (22.3.30), we obtain Kk1 ¼ Pk1 ()HTk1 ½Hk1 Pk1 ()HTk1 þ Rk1 1 , k [ {1, 2, . . . , N}
(22:3:40)
2. The error covariance update equation [Eq. (22.3.31)] Pk (þ) ¼ (I Kk Hk )Pk (),
k [ {1, 2, . . . , N}
(22:3:31)
can be expressed in an alternate form as 1 T 1 P1 k (þ) ¼ Pk () þ Hk Rk Hk
(22:3:41)
3. The Kalman gain matrix Kk given by Eq. (22.3.30) Kk ¼ Pk ()HTk ½Hk Pk ()HTk þ Rk 1 ,
k [ {1,2, . . . , N}
(22:3:30)
can also be expressed in an alternate form as Kk ¼ Pk (þ)HTk R1 k
(22:3:42)
4. If the signal and observation noises are not assumed Gaussian, then the variance equation will be a conditional variance that will be dependent on the third conditional moment. The Gaussian assumption renders the third moments zero, resulting in an unconditional variance. Hence the signal estimate equation [Eq. (22.3.38)] and the variance equation [Eq. (22.3.39)] are not coupled as stated earlier. As a consequence, the Kalman gain and covariance matrix can be
FIGURE 22.3.5
22.3 Discrete Kalman Filter
655
processed independently of the observations or the state estimate. This is of immense value in navigation problems. The propagation of the covariance matrix Pk(þ) and the evaluation of the Kalman gain Kk are shown in Fig. 22.3.5. The Kalman filter algorithm is summarized in the following list: Summary of Discrete Kalman Filter Algorithm In this list N stands for the discrete data set N ¼ f1, 2, . . . , Ng. Signal or State Process: Xk ¼ Fk, k1 Xk1 þ Gk1 Wk1 ;
k [ N; X0
(22:3:9)
Observation or Measurement Process: Yk ¼ Hk Xk þ Vk ,
k [ N; Y0 ¼ 0
(22:3:10)
Signal Error Process: ^ k Xk Xk ¼ X Prior Statistics: E½Wk ¼ E½Vk ¼ 0; E½Wk WTk ¼ Qk , E½Vk VTk ¼ Rk E½Wk VTk ¼ E½Wk XT0 ¼ E½Vk XT0 ¼ 0
(22:3:43)
cov½X0 ¼ E½X0 XT0 ¼ P0 ; cov½Xk ¼ E½Xk XTk ¼ Pk Filter Algorithm Across Observations Variance Update Equation: Pk (þ) ¼ (I Kk Hk )Pk (1) or T 1 ¼ P1 k () þ Hk Rk Hk ,
P1 k (þ) k[N
(22:3:12b)
Kalman Gain Update Equation: Kk ¼ Pk ()HTk ½Hk Pk ()HTk þ Rk 1 ¼ Pk (þ)HTk R1 k ,
k[N
(22:3:13)
Signal Estimate Update Equation: ^ k (þ) ¼ X ^ k (), ^ k () þ Kk ½Yk Hk X X
^ 0 ¼ X0 k [ N, X
(22:3:12a)
Between Observations Variance Extrapolation: Pk () ¼ Fk, k1 Pk1 (þ)FTk, k1 þ Gk1 Qk1 GTk1 ,
k[N
(22:3:14b)
Signal Estimate Extrapolation: ^ k () ¼ Fk, k1 X ^ k1 (þ), X Example 22.3.1
k[N
The observation process is given by a scalar equation: Yk1 ¼ Xk1 þ Vk1 , k [ {1, 2, . . . , N}
(22:3:14a)
656
Weiner and Kalman Filters
where the variance of the noise sequence fVkg is Rk ¼ var½Vk ¼ s2V and the scalar covariance matrices Pk(þ) ¼ pk(þ) and Pk(2) ¼ pk(2). We will investigate the propagation of the error variance when the next observation Yk is available. Using the more convenient covariance equation [Eq. (22.3.40)], we obtain 1 1 1 ¼ þ 2 pk (þ) pk () sV
or pk (þ) ¼
pk () 1 þ pk ()=s2V
We observe that if the observation uncertainty s2V is low, then the additional observation Yk reduces the estimation uncertainty pk(þ) drastically, or pk(þ) pk (2). On the other hand, if s2V is high, then the estimation uncertainty remains practically the same: pk (þ) pk(2). An important fact to note is that an additional observation does not degrade the estimation accuracy! The Kalman gain kk is computed from Eq. (22.3.41) as follows: kk ¼ pk (þ) 1
1 pk () 1 pk () 1 ¼ ¼ ¼ s2V 1 þ pk ()=s2V s2V s2V þ pk () 1 þ s2V =pk ()
For low measurement variance, the Kalman gain is approximately 1; for high measurement variance, the gain becomes 0. As stated earlier, the variance and the gain can be processed independently of the observations.
Example 22.3.2
A zero mean signal and observation processes are given by
Xk ¼ Xk1 ,
k [ {1, 2, . . . , N};
Yk ¼ X k þ V k ,
k [ {1, 2, . . . , N}
where fVkg is a zero mean Gaussian white noise sequence with variance s2V . The initial error variance is given by E½X^ 0 X0 2 ¼ p0 . Across Observations. The Kalman gain kk is obtained from Eq. (22.3.13) as kk ¼ pk () 1 ½1 pk () 1 þ s2V 1 ¼
pk () pk () þ s2V
and from Eq. (22.3.41) it can also be given by kk ¼ pk (þ) 1
1 pk (þ) ¼ 2 sV s2V
The variance is obtained from Eq. (22.3.12b) as pk () pk ()s2V pk () ¼ pk () ¼ pk (þ) ¼ 1 2 pk () þ sV pk () þ s2V 1 þ pk ()=s2V
(A)
which is the same as obtained in the previous example. The signal estimate is obtained from Eq. (22.3.12a) as pk (þ) pk () ½Yk X^ k () ¼ X^ k () þ ½Yk X^ k () X^ k (þ) ¼ X^ k () þ s2V pk () þ s2V Between Observations. From Eqs. (22.3.14), we obtain X^ kþ1 () ¼ X^ k (þ);
pkþ1 () ¼ pk (þ)
(B)
22.3 Discrete Kalman Filter
657
Closed-Form Solution. Substituting pk () ¼ pk1 (þ) in Eq. (A), we obtain pk (þ) ¼
pkþ1 (þ) 1 þ pk1 (þ)=s2V
The preceding difference equation can be solved as follows: p0 p1 (þ) ¼ 1 þ p0 =s2V p0 p1 (þ) p0 1 þ p0 =s2V ¼ p2 (þ) ¼ ¼ p0 2 1 þ p1 (þ)=sV 1 þ 1 þ 2p0 =s2V s2V (1 þ p0 =s2V ) and by induction the solution is pk (þ) ¼
p0 1 þ kp0 =s2V
and from Eq. (B)
pkþ1 () ¼
p0 1 þ kp0 =s2V
Hence the Kalman gain will be given by kk ¼
pk (þ) p0 ¼ 2 2 sV sV þ kp0
The signal estimate equation can be rewritten as X^ k (þ) ¼ X^ k () þ
p0 ½Yk X^ k () s2V þ kp0
and from Eq. (B) X^ kþ1 () ¼ X^ k () þ
p0 ½Yk X^ k () s2V þ kp0
Remarks. As the number k of observations increases, pkþ1(2) decreases and the estimation accuracy improves. Kalman Filter—Scalar Case
For a clearer understanding of the Kalman filter, we shall take a scalar case and derive the filter equations. Let N represent the observation interval given by N ¼ f1,2, . . . ,Ng. The signal and the observation processes for a scalar case are given by Xk ¼ fXk1 þ Wk , ¼ hk X k þ Vk ,
k[N k[N
(22:3:44)
where fWkg and fVkg are zero mean Gaussian white-noise sequences with variances s2W and s2V respectively. The initial error variance is given by E½X^ 0 X0 2 ¼ p0 . Across Observations. The Kalman gain kk is obtained from Eq. (22.3.13) as, kk ¼ pk ()hk ½h2k pk () þ s2V 1 ¼
pk ()hk pk (þ)hk ¼ h2k pk () þ s2V s2V
(22:3:45)
658
Weiner and Kalman Filters
The variance is obtained from Eq. (22.3.12b) as follows: pk ()hk hk pk ()s2V pk () ¼ 2 pk (þ) ¼ 1 2 2 hk pk () þ sV hk pk () þ s2V pk () ¼ 1 þ h2k pk ()=s2V
(22:3:46)
The signal estimate is obtained from Eq. (22.3.12a) as follows: X^ k (þ) ¼ X^ k () þ kk ½Yk hk X^ k () ¼ X^ k () þ
pk ()hk 2 hk pk () þ s2V
½Yk hk X^ k ()
(22:3:47)
Between Observations. From Eqs. (22.3.14a) and (22.3.14b), we obtain X^ k () ¼ fX^ kþ1 (þ);
pk () ¼ f2 pk1 (þ) þ s2W
(22:3:48)
Substituting for pk(2) from Eq. (22.3.48) in Eq. (22.3.46), we obtain pk (þ) ¼
1þ
f2 pk1 (þ) þ s2W 2 hk ½f2 pk1 (þ) þ s2w =s2V
(22:3:49)
Since the first observation comes at t ¼ 1, we have p0(2) ¼ p0(þ) ¼ p0 and p1(þ) is p1 (þ) ¼
f2 p0 þ s2W 1 þ h2k ½f2 p0 þ s2W =s2V
(22:3:50)
Hence pk(þ) can be solved recursively. The Kalman gain kk is given by Eq. (22.3.45). The signal estimate extrapolation is obtained from Eq. (22.3.14a) as X^ k () ¼ fX^ k1 (þ)
(22:3:51)
Example 22.3.3 The signal and observation processes are given by Eq. (22.3.44) with f ¼ 45 and hk ¼ 65. The zero mean white-noise signals have variances s2W ¼ 1 and s2V ¼ 1. The initial variance p0 ¼ 45. The initial value X0 ¼ 2.5403, and the observations fYkg for N ¼ 20 are given in Table 22.3.1. TABLE 22.3.1 k
Yk
k
Yk
0 1 2 3 4 5 6 7 8 9 10
5.5629 0.5982 2.9623 3.4911 2.3531 20.3591 20.3427 2.4036 1.3624 2.8077 3.2718
11 12 13 14 15 16 17 18 19 20
0.7010 4.2405 0.9243 1.3428 3.5725 1.3205 0.9066 2.9516 2.7597 1.8991
22.3 Discrete Kalman Filter
659
We will formulate the Kalman filter for two cycles and then use the recursive formulas. Since the first observation arrives at t ¼ 1, we have p0(2) ¼ p0(þ) ¼ p0 ¼ 45, and substituting this value of p0 in Eq. (22.3.50), we obtain Cycle 1: 2 4 4 þ1 4725 5 5 ¼ ¼ 0:4759 p1 (þ) ¼ 2 9929 4 4 2 þ1 6 5 5 1þ 1 5 The Kalman gain k1 is obtained from Eq. (22.3.45) as k1 ¼
p1 (þ)hk 4725 6 5670 ¼ ¼ 0:5711 ¼ 9929 5 9929 1
Since X^ 0 (þ) ¼ X0 ¼ 2:5403, we can determine X^ 1 () from Eq. (22.3.48) as X^ 1 () ¼ 0:8 2:5403 ¼ 2:0322. Hence X^ 1 (þ) is obtained from Eq. (22.3.47) as follows: X^ 1 (þ) ¼ X^ 1 () þ k1 ½Y1 h1 X^ 1 () ¼ 2:0322 þ 0:5711 (0:5982 1:2 2:0322) ¼ 0:9811 Cycle 2. From Eq. (22.3.49), we have 2 4 4725 þ1 323, 825 5 9929 ¼ ¼ 0:4532 p2 (þ) ¼ 2 714, 533 4 4725 2 þ1 6 5 9929 1þ 1 5 The Kalman gain k2 is obtained from Eq. (22.3.45) as k2 ¼
p2 (þ)hk 323, 825 6 388, 590 ¼ ¼ 0:5438 ¼ 714, 533 5 714, 533 1
Since X^ 1 (þ) ¼ 0:9811, we can determine X^ 2 () from Eq. (22.3.48) as X^ 2 () ¼ 0:8 0:9811 ¼ 0:7849 Hence X^ 2 (þ) is obtained from Eq. (22.3.47) as follows: X^ 2 (þ) ¼ X^ 2 () þ k2 ½Y2 h2 X^ 2 () ¼ 0:7849 þ 0:5438 (2:9623 1:2 0:7849) ¼ 1:8836 We can now use the recursive formulas [Eqs. (22.3.49), (22.3.45), and (22.3.47)] to obtain pk(þ), kk and X^ k (þ) respectively for observation interval N ¼ 20. Figure 22.3.6 shows the simulated signal process Xk, the observation data Yk, and the estimated data X^ k (þ) for k ¼ 0, . . . , 20. Figure 22.3.7 shows the Kalman gain kk for k ¼ 0, . . . ,10. The figure shows that from the fifth observation the gain is a constant at 0.5415 and further observations do not improve the estimation accuracy.
660
Weiner and Kalman Filters
FIGURE 22.3.6
FIGURE 22.3.7
B 22.4
CONTINUOUS KALMAN FILTER
We can now derive the analogous Kalman filter for the continuous-time problem. The continuous linear signal process can be represented in the time interval T by an n-vector stochastic differential equation dX(t) ¼ F(t)X(t) þ G(t)W(t), dt
t [ T, X0
(22:4:1)
The corresponding linear observation process is given by Y(t) ¼ H(t)X(t) þ V(t),
t[T
(22:4:2)
^ for the signal X(t) Given the observations fY(t), t [ Tg, we have to find the estimate X(t) under the optimality criteria of unbiasedness and minimum variance, and this estimate is ^ ¼ E½X(t) j Y(t), t [ T. will be given by the conditional expectation X(t) Assumptions 1. The n-vector process {X(t), t [ T} and the m-vector process {Y(t), t [ T} are random processes defined on a suitable probability space.
22.4
Continuous Kalman Filter
661
2. F(t) is a nonsingular n n matrix, G(t) is an n r rectangular matrix, and H(t) is an m n rectangular matrix such that ð jFij (t)jdt , 1, i ¼ 1, 2, . . . , n, j ¼ 1, 2, . . . , n t[T
ð
jGij (t)j2 dt , 1, i ¼ 1, 2, . . . , n,
j ¼ 1, 2, . . . , r
jHij (t)jdt , 1,
j ¼ 1, 2, . . . , n
(22:4:3)
t[T
ð
i ¼ 1, 2, . . . , m,
t[T
3. W(t) is an r-vector of zero mean Gaussian white-noise processes with covariance parameter matrix Q(t), given by Q(t) ¼ E[W(t)W T(t)]dt. 4. V(t) is an m-vector of zero mean Gaussian white-noise processes with covariance parameter matrix R(t), given by R(t) ¼ E[V(t)V T(t)]dt. 5. The noise vectors W(t) and V(t) are independent, or E[W(t)V T(t)] ¼ 0. 6. The Gaussian initial condition vector X0 is independent of both the noise vectors W(t) and V(t), or E[W(t)X T0 ] ¼ 0 and E[V(t)X T0 ] ¼ 0. ^ is the expectation of 7. If U(t) is any random process vector, then the estimate U(t) U(t) conditioned on the observation fY(s), s tg and will be represented by ^ ¼ E½U(t) j Y(s), Et ½U(t) ¼ U(t)
s t:
8. The innovations process fn(t), t [ Tg is an m-vector white-noise process given by ^ n(t) ¼ Y(t) H(t)X(t),
t[T
(22:4:4)
n o ^ X(t)½X(t) ^ X(t)T is positive 9. The error covariance matrix P(t) ¼ E ½X(t) definite. Under these assumptions the continuous Kalman filter can be given by the following equations. Since this is a continuous-time process, we cannot distinguish between ^ across and between observations. The conditional expectation Et ½X(t) ¼ X(t) satisfies the stochastic differential equation d ^ ^ þ K(t)½Y(t) H(t)X(t), ^ X(t) ¼ F(t)X(t) dt
t[T
(22:4:5)
and the covariance matrix P(t) satisfies the stochastic matrix Riccati equation d P(t) ¼ F(t)P(t) þ P(t)FT (t) þ G(t)Q(t)GT (t) K(t)H(t)P(t), dt
t[T
(22:4:6)
where the Kalman gain matrix K(t) satisfies K(t) ¼ P(t)HT (t)R1 (t),
t[T
(22:4:7)
We can show these results by discretizing the differential equations and comparing them to the discrete Kalman filter. Assuming a uniform discretization interval of Dt ¼ tk 2 tk21, k ¼ 1, . . .,N, we can write Eq. (22.4.1) as Xk ¼ (I þ Fk1 Dt)Xk1 þ Gk1 Wk1 Dt,
k [ {1, 2, . . . , N}, X0
(22:4:8)
662
Weiner and Kalman Filters
where the subscript k stands for the value of the processes at time t ¼ tk. Comparing Eq. (22.4.8) to Eq. (22.3.9), we can identify I þ Fk1 Dt ¼ Fk, k1 ;
Gk1 Dt ¼ Gk1
(22:4:9Þ
From assumptions 3 and 4, the noise covariance matrices are given by E½W(t)WT (t) ¼
Q(t) ; dt
E½V(t)VT (t) ¼
R(t) dt
(22:4:10)
As the discretization interval is Dt, the covariance matrices of W(t) and V(t) at time tk can be given in terms of the discrete covariance matrices Qk and Rk as Q(tk ) ¼ Qk ; Dt
R(tk ) ¼ Rk Dt
(22:4:11)
Since the limit will be taken as Dt ! 0, the distinction between tk(þ) and tk(2) will be eliminated and written as tk(þ) ¼ tk(2) ¼ tk. After substituting Eqs. (22.4.9) and (22.4.11), the Kalman gain equation [Eq. (22.3.40)] can be rewritten for k ¼ 1, . . .,N: R(tk1 ) 1 Kk1 ¼ Pk1 HTk1 Hk1 Pk1 HTk1 þ Dt
1 ¼ Pk1 HTk1 Hk1 Pk1 HTk1 Dt þ Rk1 Dt
(22:4:12)
Dividing Eq. (22.4.12) by Dt and taking the limit as Dt ! 0, we obtain lim
Dt!0
Kk1 ¼ K(t) ¼ P(t)HT (t)R1 (t), Dt
t[T
(22:4:13)
which is Eq. (22.4.7). Similarly, substituting Eqs. (22.4.9) and (22.4.11) in the covariance equation [Eq. (22.3.39)], we can write Pk ¼ ½I þ Fk1 Dt½ðI Kk1 Hk1 )Pk1 ½I þ Fk1 DtT þ Gk1 Dt
Q(tk ) T Gk1 Dt Dt
¼ ½I þ Fk1 DtPk1 ½I þ Fk1 DtT ½I þ Fk1 Dt½Kk1 Hk1 Pk1 ½I þ Fk1 DtT þ Gk1 Q(tk )GTk1 Dt,
k [ {1, 2, . . . , N}
(22:4:14)
Substituting for Kk21 ¼ K(t)Dt from Eq. (22.4.13) in Eq. (22.4.14), we obtain Pk Pk1 ¼ Pk1 FTk1 Dt þ Fk1 Pk1 Dt þ Gk1 Q(tk )GTk1 Dt K(t)Hk1 Pk1 Dt þ o(Dt)
(22:4:15)
where limDt!0 o(Dt) ! 0. Dividing Eq. (22.4.15) by Dt and taking the limit as Dt ! 0, we obtain lim
Dt!0
Pk Pk1 d ¼ P(t) ¼ P(t)FT (t) þ F(t)P(t) þ G(t)Q(t)GT (t) dt Dt K(t)H(t)P(t),
which is Eq. (22.4.6).
t[T
(22:4:16)
22.4
Continuous Kalman Filter
663
FIGURE 22.4.1
Finally, substituting the same Eqs. (22.4.9) and (22.4.11) in the state estimate equations [Eq. (22.3.38)], we can write ^ k ¼ ½I þ Fk1 Dt{X ^ k1 } ^ k1 þ Kk1 ½Yk1 Hk1 X X ^ k1 Dt þ Kk1 ½Yk1 Hk1 X ^ k1 ^ k1 þ Fk1 X ¼X ^ k1 , þ Fk1 DtKk1 ½Yk1 Hk1 X
k [ {1, 2, . . . ,N}
(22:4:17)
Substituting for Kk21 ¼ K(t)Dt from Eq. (22.4.13) in Eq. (22.4.17), we obtain ^k X ^ k1 ¼ Fk1 X ^ k1 Dt þ K(t)Dt½Yk1 Hk1 X ^ k1 þ o(Dt) X
(22:4:18)
Dividing Eq. (22.4.18) by Dt and taking the limit as Dt ! 0, we obtain ^k X ^ k1 X d ^ ^ þ K(t)½Y(t) H(t)X(t), ^ ¼ F(t)X(t) ¼ X(t) Dt!0 dt Dt lim
t[T
(22:4:19)
which is Eq. (22.4.5). The block diagram implementing the continuous Kalman filter is shown in Fig. 22.4.1. Example 22.4.1 This example is the continuous analog of the discrete example (Example 22.3.2). The zero mean signal and observation processes are given by d X(t) ¼ 0, dt
t [ T;
Y(t) ¼ X(t) þ V(t),
t[T
where V(t) is a zero mean white-noise process with variance s2V. The variance of the initial state is p0. In this example F(t) ¼ 0, G(t) ¼ 0 and H(t) ¼ 1. The Kalman gain k(t) is obtained from Eq. (22.4.7): k(t) ¼
p(t) s2V
The variance is obtained from Eq. (22.4.6): d p2 (t) p(t) ¼ 2 dt sV
or
dp(t) dt ¼ 2 2 p (t) sV
664
Weiner and Kalman Filters
Integrating both sides of the equation from p0 to p(t), we have ð p(t) p0
dp(t) 1 ¼ 2 p2 (t) sV
ðt dt
or p(t) ¼
0
p0 1 þ p0 t=s2V
The signal estimate from Eq. (22.4.5) is given by d ^ p0 ^ X(t) ¼ 2 ½Y(t) X(t) dt sV þ p 0 t Example 22.4.2 We will formulate a continuous version of Example 22.3.3. The signal and observation processes are given by d X(t) ¼ X(t) þ W(t), X0 ; dt
Y(t) ¼ 2X(t) þ V(t)
The zero mean noise signals W(t) and V(t) are independent Gaussian-distributed processes with the variance parameters E[W 2(t)]dt ¼ 1 and E[V 2(t)]dt ¼1. The error variance from Eq. (22.4.6) is d p(t) ¼ 2p(t) 2p2 (t) þ 1, p0 dt
(A)
This equation is a Riccati equation, and one method of solving it is to convert it to a linear differential equation. Otherwise, it can be solved numerically. The Kalman gain and the state estimate hinge on finding this soution. Following the procedure in Ref. 57, we will assume a solution to the preceding equation in the form p(t) ¼ a þ b tanh(ct þ d) where a,b,c,d are constants to be determined. Differentiating p(t) and substituting in Eq. (A), we obtain bc½1 tanh2 (ct þ d) ¼ 1 2½a þ b tanh(ct þ d) 2½a þ b tanh(ct þ d)2 ¼ 1 2a 2a2 2b(1 þ 2a) tanh(ct þ d) 2b2 tanh2 (ct þ d) Equating like coefficients of tanh(ct þ d ) on both sides of the equation above, we obtain pffiffiffi pffiffiffi 3 1 a¼ ; b¼+ ; c¼+ 3 2 2 Since p(t) as a variance function has to be positive, we use the positive values for b and c in p(t) and write pffiffiffi pffiffiffiffi 3 1 p(t) ¼ þ tanh( 3t þ d) 2 2 where the unknown constant d has to be obtained from the initial condition p0. Thus 2p0 þ 1 2p0 þ 1 pffiffiffi tanh(d) ¼ pffiffiffi ; d ¼ tanh1 3 3
22.4
Continuous Kalman Filter
665
FIGURE 22.4.2
Hence pffiffiffi pffiffiffiffi 3 1 1 2p0 þ 1 pffiffiffi p(t) ¼ þ tanh 3t þ tanh , 2 2 3
0t,1
(B)
p(t) is shown in Fig. 22.4.2 for values of the initial condition p0 ¼ 0,0.2,0.4,0.6,0.8,1. From Eq. (B) and from Fig. 22.4.2 we see that as t ! 1, p(t) tends to constant given by pffiffiffi pffiffiffi 3 31 1 lim p(t) ¼ þ tanh½1 ¼ t!1 2 2 2 which is independent of the initial condition. This is a desirable situation where the final value of the uncertainty is a constant independent of the initial conditions because they may not be known a priori to any degree of certainty. From Eq. (22.4.7), the Kalman gain k(t) is given by k(t) ¼ 2p(t) The signal estimate is obtained from Eq. (22.4.5): d ^ ^ þ 2p(t)½Y(t) 2X(t) ^ X(t) ¼ X(t) dt ^ will be independent of the initial Here also as t ! 1, the final value of the estimate X(t) condition.
CHAPTER
23
Probabilistic Methods in Transmission Tomography
Probabilistic methods are used extensively in medical imaging. In this chapter Bayesian reconstruction algorithms for transmission tomographic images are presented. The reconstruction is based on maximum a posteriori (MAP) estimation using the expectation – maximization (EM) algorithm. Gibbs’ density is used as the prior distribution with a sigmoidal potential function. The reconstruction problem itself is solved through a one-step-late expectation –maximization algorithm (EM-OSL) proposed by Green [9].† Suitable approximations are made to render the problem more tractable for parallel computation. Computer-simulated phantoms are used for image reconstruction. These images are compared to those reconstructed from currently available reconstruction algorithms in terms of image quality and convergence rate. This algorithm works quite well for low-photon-count and low-contrast cases in which the widely used convolution backprojection algorithm performs rather poorly.
B 23.1
INTRODUCTION
In transmission tomography (TT) the acquired projection data contain high statistical variations due to limited dose of radiation introduced in patients. Deterministic reconstruction techniques such as convolution backprojection and other Fourier-based methods introduce various types of artifacts. Transmission tomography models for reconstruction takes into account the probabilistic nature of the problem by incorporating statistical variations and other physical phenomena in a stochastic model. Application of the maximum-likelihood estimation to image reconstruction problem was recognized by Rockmore and Macovski [1,2]. An alternative to the maximum-likelihood approach was provided by Dempster et al. [3] with a general iterative method known as the expectation –maximization (EM) algorithm. The relevance of the EM algorithm to maximum-likelihood image reconstruction This chapter is based on the work [14] carried out by the author and his student at the Center of Advanced Computation and Telecommunications, Univ. Massachusetts Lowell and reported in an internal memorandum, Modified EM Algorithm for Transmission Tomography, T94-I, 1994. † References cited in this chapter are listed in Section 23.8 (not in the References list at the end of the book).
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
666
23.2
Stochastic Model
667
was noticed by Shepp and Vardi [4], who applied it to a stochastic model of positron emission tomography (PET), and independently by Lange and Carson [5], who applied it to general models of emission and transmission tomography. The initial optimism of the EM method of reconstruction in emission tomography has been tempered by the grainy, speckled appearance of the reconstructed images. This graininess is a manifestation of low correlations between parameter estimates for neighboring pixels. A satisfying solution is to incorporate some smoothness criterion in the reconstruction process itself. In emission tomography, this perspective has been advocated by Geman and McClure [6,7], Hebert and Leahy [8], and Green [9], who build on previous works of Geman and Geman [10] and Besag [11]. In particular, Geman and McClure [6,7] introduce Gibbs’ priors with nearest-neighbor interactions. Green [9] devises an approximate EM algorithm incorporating nearest-neighbor interactions with the one-step-late expectation– maximization (EM-OSL) algorithm. Lange [12] has demonstrated the conditions under which the EM-OSL algorithm converges to the unique maximum posterior point in image space for both emission and transmission cases. In this chapter Gibbs’ prior with sigmoidal potential function is developed that applies the EM-OSL scheme of Green [9] to the transmission algorithm proposed by Lange [12]. In simplifying the resultant transcendental equation, three terms have been used in the series expansion rather than the two in Lange [12]. The solution of the resulting quadratic equation yields more accurate results. The sigmoidal potential function smooths out significant differences in neighboring pixel values while retaining sharp-edged features of images.
B 23.2
STOCHASTIC MODEL
Notation
Before describing the model in detail, some of the notations are defined in Fig. 23.2.1:
i
Projection subscript
K j
Number of projection paths Ordered pixel subscript for each projection path i starting from the pixel farthest from the detector i
fIig fJjg
Set of (m 2 1) pixels contributing to the projection path i Set of projections to which pixel j contributes
Yi
Random variable for detected photons from projection i, or Yi ¼ Xim
Xij X^ ij
Random variable for photons from projection i entering pixel j Random variable for photons from projection i leaving pixel j, or entering pixel j þ 1, that is, X^ ij ¼ Xijþ1
Mj mj
Random variable for attenuation coefficient of pixel j Realization of Mj
lij N
Pathlength of projection i through pixel j Neighboring pixels
The subscript i in mi has been omitted for clarity of notation.
668
Probabilistic Methods in Transmission Tomography
FIGURE 23.2.1 Probability Model
Corresponding to each projection i, the source photons are assumed to travel along a thin path en route to the detector. This path cuts through a set fIig of (m 21) pixels ordered by the index j; that is, j ¼ 1 is the pixel nearest to the source, and pixel j ¼ m21 is the last pixel along the path. The attenuation of pixel j is a random variable Mj taking the value mj and the length of the path inside pixel j is lij. Photons traveling along path i are counted as Xij at the entrance to pixel j and as X^ ij ¼ Xijþ1 at the exit, with Xij X^ ij photons becoming attenuated in pixel j. The number of photons Xim leaving the last pixel (m 2 1) is the same as the number of photons Yi being detected by the detector i, or Xim ¼ Yi. The attenuation coefficients fmjg have to be estimated from the observed photon count Xim ¼ Yi. The random variable Xi1 of photons leaving the source at projection i is assumed to be Poisson-distributed as P(Xi1 ¼ xi1 ) ¼ edi
dixi1 xi1 !
(23:2:1)
where di ¼ E[Xi1] is the mean value of photons leaving the source in projection i. The probability that a photon entering pixel j along path i will survive the attenuation at pixel j, conditioned on the attenuation coefficient Mj ¼ mj, is assumed to be Pj ¼ emj lij . The probability that X^ ij photons will emanate from pixel j conditioned on both Xij ¼ xij photons entering pixel j, and on the attenuation coefficient Mj ¼ mj, can be characterized by a binomial distribution: xij P{X^ ij ¼ x^ ij j Xij ¼ xij j Mj ¼ mj } ¼ (23:2:2) (emj lij )x^ ij (1 emj lij )(xij ^xij ) x^ ij From Eq. (23.2.1) the conditional mean value, gij ¼ E[Xij j m1, m2, . . . , mj21] of the photons, Xij entering pixel j along the projection i can be given by j1
gij ¼ E½Xij j m j1 ¼ di (em1 li1 )(em2 li2 ) (em j1 lij1 ) ¼ di eSk¼1 mk lik
(23:2:3)
where mj21 represents the attenuation coefficient vector mj21 ¼ [m1, m2, . . . , mj21]T. Similarly, the conditional mean value, gim ¼ E[Xim j m1, m2, . . . , mm21] of the detected photons Xim is given by m1
gim ¼ E½Xim j mm1 ¼ di (em1 li1 )(em2 li2 ) (emm1 lim1 ) ¼ di eSk¼1 mk lik
(23:2:4)
23.2
Stochastic Model
669
where mm21 represents the attenuation coefficient vector mm21 ¼ [m1, m2, . . . , mm21]T. The ratio gim/gij can be expressed as follows: Pm1 Pm1 gim E½Xim j mm1 di e k¼1 mk lik mk lik k¼j ¼ ¼ ¼e P j1 gij m l ik k k¼1 di e E½Xij j mj1
(23:2:5)
Since the input random variable Xij to pixel j conditioned on mj21 ¼ [m1, m2, . . . , mj21]T is Poisson-distributed with mean gij given by Eq. (23.2.3), the conditional distribution of Xij is x
P½Xij ¼ xij j m j1 ¼ egij
gijij xij !
(23:2:6)
The probability of the detected photons Xim conditioned on Xij photons entering pixel j, and the attenuation vector mm21 ¼ [m1, m2, . . . , mm21]T, can again be given by a binomial distribution similar to Eq. (23.2.2): P{Xim ¼ xim j Xij ¼ xij j mm1 } Pm1 xim ðxij xim Þ Pm1 xij mk lik mk lik k¼j k¼j e 1e ¼ xim
(23:2:7)
The ratio gim =gij from Eq. (23.2.5) is substituted in Eq. (23.2.7) and rewritten as P{Xim ¼ xim j Xij ¼ xij j mm1 } ¼
xij xim
gim gij
!xim
g 1 im gij
!(xij xim ) (23:2:8)
Using Bayes’ theorem [Eq. (2.4.4)] the probability of Xij conditioned on the detected photons Xim ¼ xim ¼ yi, and the attenuation coefficient vector mm21 can be written as P{Xij ¼ xij j Xim ¼ xim j mm1 } ¼
¼
P{Xim ¼ xim j Xij ¼ xij j mm1 }P{Xij ¼ xij j mm1 } P{Xim ¼ xim j mm1 } !xim !(xij xim ) x g ij xij ! gim gim gij ij 1 e xim !(xij xim )! gij gij xij ! egim
gximim xim !
(23:2:9)
Simplifying Eq. (23.2.9), we obtain a conditional Poisson distribution P{Xij ¼ xij j Xim ¼ xim j mm1 } ¼
1 (g gim )(xij xim ) e(gij gim ) (xij xim )! ij
(23:2:10)
The mean of the photons Xij conditioned on the observed detected photons Xim ¼ xim ¼ yi, and attenuation coefficient vector mm21 can be obtained from by taking the expected
670
Probabilistic Methods in Transmission Tomography
value of Eq. (23.2.10): E½Xij j Xim ¼ Yi ¼ xim j mm1 ¼
1 X xij
¼
xij (g gim )(xij xim ) e(gij gim ) (x xim )! ij ¼0 ij
1 X xij xim þ xim xij ¼0
(xij xim Þ!
(gij gim )(xij xim ) e(gij gim )
¼ gij gim þ xim ¼ gij gim þ yi 4 Nij ¼
(23:2:11)
We will now find the joint conditional probability of the photons from projection i entering pixels f j, j ¼ m, m 2 1, . . . , 1g conditioned on the attenuation coefficient vector mm21. Defining an m-vector Xi ¼ fXim, Xim21, . . . , Xi1gT, the joint conditional probability, PfXi j mm21g of Xi conditioned on the attenuation coefficient vector mm21, can be evaluated by using the Markov property of Xi: P{Xi j mm1 } ¼ P{Xim , Xim1 , . . . , Xi1 j mm1 } ¼ P{Xim j Xim1 j mm1 }P{Xim1 j Xim2 j mm2 } P{Xi2 j Xi1 j m1 }P{Xi1 }
(23:2:12)
But by analogy to Eq. (23.2.2), the conditional probability of Xikþ1 conditioned on Xik and mk is given by a binomial distribution: P{Xikþ1 ¼ xikþ1 j Xik ¼ xik j Mk ¼ mk } xik ¼ (emk lik )xikþ1 (1 emk lik )(xik xikþ1 ) xikþ1 Substituting Eqs. (23.2.13) and (23.2.1) into Eq. (23.2.12), we obtain " # m 1 Y dxi1 xik mk lik x^ ik mk lik (xik ^xik ) ) (1 e ) edi 1 (e P{Xi j mm1 } ¼ x^ ik xi1 ! k¼1
(23:2:13)
(23:2:14)
where we have used x^ ik ¼ xikþ1 . Equation (23.2.14) represents the conditional probability for the projection path i. Similar conditional probabilities can be formulated for all the K projection paths. We define matrices X ¼ {X1 , X2 , . . . , XK }T , Y ¼ {Y1 , Y2 , . . . , YK }T , and M ¼ {the set of attenuation coefficient vectors mmi 1 for all projections i ¼ 1, . . . , K}. Summing Eq. (23.2.14) over all projections i, the following conditional probability can be written as " # K m 1 Y Y d xi1 xik mk lik x^ ik mk lik (xik ^xik ) (23:2:15) ) (1 e ) P{X j M} ¼ (e edi i x^ ik xi1 ! i¼1 k¼1 and the loglikelihood function ln P{X j M} can be written as ln P{X j M} ¼
K X m1 X
x^ ik ln (emk lik ) þ (xik x^ ik ) ln (1 emk lik ) þ C
(23:2:16)
i¼1 k¼1
where the constant C represents the logarithm of terms independent of mk. The estimate of the attenuation matrix M is obtained by maximizing the posterior probability of M conditioned on the observed but incomplete data Y, that is P{M j Y}
23.3
Stochastic Estimation Algorithm
671
or the corresponding loglikelihood function ln P{M j Y}. However, maximizing ln P{M j Y} is an intractable problem. The problem is mitigated by embedding the observed but incomplete space Y into the complete but unobservable space X. Since X is connected to Y by a many-to-one mapping, the richer space X provides more flexibility in data manipulation, resulting in an easier maximization of ln P{M j X}. Using Bayes’ theorem, the likelihood function P{M j X} will be given by P{M j X} ¼
P{X j M}P{M} P{X}
(23:2:17)
and the corresponding loglikelihood function is ln P{M j X} ¼ ln P{X j M} þ ln P{M} ln P{X}
(23:2:18)
Equations (23.2.17) or (23.2.18) will form the basis for the estimation procedure.
B 23.3
STOCHASTIC ESTIMATION ALGORITHM
The maximum-likelihood estimator for M is obtained by assuming that the attenuation coefficients {mk } are equiprobable, in which case PfMg is constant. Since P{X} is independent of M, maximizing P{M j X} amounts to maximizing the loglikelihood function ln P{X j M} given in Eq. (23.2.16) with respect to M. Since it contains the unknown values of random variables Xik and X^ ik, direct maximization of Eq. (23.2.16) will not give meaningful results. Hence its expected value will be maximized with respect to {mk }. From Eq. (23.2.11), the conditional expectations of Xik and X^ ik are functions of {mk } via {gik }, and hence an iterative method of maximizing Eq. (23.2.18) has to be developed. The projection matrix Y is some function g(X) where g(.) is a many-to-one mapping from the space of X to the space of Y. Hence the iterative solution to the maximumlikelihood estimation problem will proceed in alternating steps of expectation and maximization. First the expectation of ln P{X j M} is taken conditioned on the observation matrix Y and the current estimate M(n). This conditional expected value E½ln P{X j M} j Y, M(n) is maximized with respect to M to yield an updated estimate M(nþ1). The updated conditional expected value E½ln P{X j M} j Y, M(nþ1) is again maximized and the process repeated. According to Dempster [3], the loglikelihood function ln P{M j Y} or equivalently ln P{Y j M} will increase in value at each iteration step with the result that ln P{Y j M(nþ1) } ln P{Y j M(n) }
(23:3:1)
and the equality holds only when the maximum-likelihood solution Mmax is reached and the maximum value is ln P{Y j Mmax }. Expectation Step
If M(n) is the estimate of the attenuation matrix M at the nth iteration, the expectation of ln P{X j M} at the (n þ 1)st iteration, conditioned on the observation Y and M(n), can be written as K X m1 h i X N^ ik ln (emk lik ) þ (Nik N^ ik ) ln (1 emk lik ) þ C1 E ln P{X j M} j Y, M(n) ¼ i¼1 k¼1
(23:3:2)
672
Probabilistic Methods in Transmission Tomography
^ conditioned on Y and M(n) are obtained where the conditional expectations Nik and Nik from Eq. (23.2.11) as Nik ¼ E Xik j Yi j M(n) ¼ gik gim þ yi h i N^ ik ¼ E X^ ik j Yi j M(n) ¼ gikþ1 gim þ yi
(23:3:3)
where Xˆik ¼ Xikþ1 and C1 is yet another constant independent of {mk }. Maximization Step
Since Nik and N^ ik are known, E½ln P{X j M} j Y, M(n) can now be maximized by differentiating Eq. (23.3.2) with respect to mk and setting it equal to 0: X @ lik E ln P{X j M} j Y, M(n) ¼ N^ ik lik þ (Nik N^ ik ) m l ¼0 @mk e k ik 1 i[Jk
(23:3:4)
Equation (23.3.4) is a transcendental equation that can be solved by approximating the exponential term by using the first three terms in the series expansion: 1 emk lik
1
¼
1 1 1 þ mk lik þ o½(mk lik )2 mk lik 2 12
(23:3:5)
Expectation– Maximization Algorithm
Substitution of Eq. (23.3.5) in Eq. (23.3.4) results in "
" # # " # X 1 X 1X 2 2 ^ ^ ^ (Nik Nik )lik mk (Nik þ Nik )lik mk þ (Nik Nik ) ¼ 0 12 i[Jk 2 i[Jk i[Jk B
A
or
C
Am2k Bmk þ C ¼ 0
for all k
(23:3:6)
Solving for mk in the quadratic equation [Eq. (23.3.6)] yields the (n þ 1)st estimate mk(nþ1) mk(nþ1)
¼
B+
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi B2 4AC 2A
j
m(n) k
for all k
(23:3:7)
where the coefficients A, B, and C are dependent on m(n) k . The smaller root has to be chosen as the solution in this algorithm so that for lik mk , 6 this solution will always fall inside the solution bounds established by Lange [5] for all values of k and i. max As n ! 1 in Eq. (23.3.7), m(n) k ! mk , and hence it is the desired expectation – maximization (EM) algorithm. EM-One-Step-Late (OSL) Algorithm
Equation (23.3.7) represents the maximum-likelihood solution with equiprobable attenuation coefficients mk(nþ1) . This solution can be considerably refined by using prior information PfMg about the attenuation coefficients M, giving rise to the maximum
23.3
Stochastic Estimation Algorithm
673
a posteriori solution. In this case, in Eq. (23.2.18) only P{X} does not depend on M. Applying the same iterative techniques to Eq. (23.2.18) as used in deriving Eq. (23.3.4), there results an equation similar to Eq. (23.3.4) as shown below: @ @ @ E ln P{M j X} j Y, M(n) ¼ E ln P{X j M} j Y, M(n) þ ln P{M} @mk @mk @mk X lik @ ^ ^ ¼ Nik lik þ (Nik Nik ) m l ln P{M} ¼ 0 þ ik k @mk e 1 i[Jk (23:3:8) Using the same series approximation for the exponential in Eq. (23.3.8) as in Eq. (23.3.5) leads to the equation "
" # # X 1 X 1 @ (Nik N^ ik )l2ik m2k (Nik N^ ik )lik ln P{M} mk 12 i[Jk 2 i[Jk @mk " þ
A
X
#
B
(Nik N^ ik ) ¼ 0
(23:3:9)
i[Jk C
where A, B, and C are the same quantities defined in Eq. (23.3.6), Eq. (23.3.9) can be rewritten as follows: @ Am2k B ln P{M} mk þ C ¼ 0 @mk
for all k
(23:3:10)
Solving for mk in the preceding quadratic yields the result vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !2 u u @ @ t B ðnþ1Þ ln P{M} + B ðnþ1Þ ln P{M} 4AC @mk @mk ¼ (n) 2A mk !
mk(nþ1)
(23:3:11)
Equation (23.3.11) cannot be solved since it involves the derivative of the log-prior distribution at mk(nþ1) . Green [9] resolves the problem by using the derivative at m(n) k yielding the EM one-step-late algorithm (EM-OSL). Additionally, the derivative of the prior P(M) is multiplied with a parameter b to add flexibility on the influence of the prior on the algorithm. With these adjustments, Eq. (23.3.11) can be rewritten as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi !2 u u @ @ B b ðnÞ ln P{M} + t B b ðnÞ ln P{M} 4AC @mk @mk ¼ (n) 2A mk !
mk(nþ1)
(23:3:12)
In Eq. (23.3.12) the smaller root of Eq. (23.3.12) is chosen in this iterative algorithm for the same convergence reasons as in Eq. (23.3.7). The optimum value of b will depend on both the characteristics of the image and the prior used. If b ¼ 0, then Eq. (23.3.12) is the EM algorithm of Eq. (23.3.7).
674
Probabilistic Methods in Transmission Tomography
B 23.4
PRIOR DISTRIBUTION PfMg
The prior distribution PfMg in Eq. (23.3.12) has to be carefully chosen. From consideration of Markovian fields, a proper choice is the exponential distribution given by Ð Pfmg ¼ (1/z)e 2V(m), m 0, where z ¼ m0 eV(m) dm is the normalization constraint. This is known as the Gibbs distribution discussed in Section 7.15. The potential function V(m) is designed to penalize large differences in estimated parameters for neighboring pixels. The constraints on the potential functions in Section 7.15 are discussed by Lange [12]. In this application, a first-order neighborhood interaction of the form X V(m) ¼ w jk v(mj mk ), (23:4:1) ð j, kÞ[N N neighboring pixels
is considered where the weighting functions wjk for a first-order Markovian field are defined by 8 < p1: ffiffiffi orthogonal pixel w jk ¼ 1= 2: diagonal pixel : 0: otherwise and are shown in Fig. 23.4.1. With Eq. (23.4.1), the prior distribution Pfmg is of the form P 1 (j, k)[N wjk v(mj mk ) P{m} ¼ e , m0 z
(23:4:2)
Two potential functions v(m) are considered here and their performances compared. The first is the proposed logarithm of hyperbolic cosine proposed by Green [9] with a parameter j given by v(m) ¼ ln cosh(jm) ¼ ln
ejm þ ejm 2
(23:4:3)
The second is the proposed sigmoidal function with a positive parameter j given by v(jm) ¼
2 1 1 þ ejm2
FIGURE 23.4.1
(23:4:4)
23.4
Prior Distribution PfMg
675
The parameter j ¼ 0 in both of these potential functions amounts to a straight EM algorithm without any prior. Figures 23.4.2 show the sigmoid function and its derivatives for values of the adjustable parameter j ¼ 2000, 3000, 4000, 5000, 6000. By varying this parameter, it is possible to smooth out speckles in the reconstructed images while retaining the sharp edges. This fact will be demonstrated analytically from the series expansion of Eq. (23.3.12). Defining, for convenience B1 ¼ B b
@ ln P{M} @m(n) k
(23:4:5)
Eq. (23.3.12) can be expanded in series in AC/B21 and written as follows: " pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1=2 # 2 4AC B + B B 4AC 1 1 1 ¼ 1 1 2 mk(nþ1) ¼ 2A 2A B1 C AC 2A2 C2 5A3 C 3 14A4 C4 42A5 C 5 ¼ 1þ 2 þ þ þ þ þ B1 B1 B41 B81 B10 B61 1
(23:4:6)
Real roots of Eq. (23.4.6) mandate that (AC/B21) , 14, and the first-order approximation for m(nþ1) can be given by k P (Nik N^ ik ) C i[J k (nþ1) mk ¼ ¼ X (23:4:7) 1 @ B1 (Nik þ N^ ik )lik b ln P{M} 2 i[Jk @mk (n) mk
Taking the derivative of the logarithm of Eq. (23.4.2) with respect to m(n) k and substituting it in Eq. (23.4.7), we obtain P (Nik N^ ik ) i[J k (23:4:8) mk(nþ1) ¼ X h i X 1 @ (n) (n) (Nik þ N^ ik )lik b w jk (n) v(mj mk ) 2 i[Jk @mk (n) j[N mk
The denominator of Eq. (23.4.8) involves the sum of two terms, one of which is the derivative of the potential function. This sum directly determines the penalty imposed on successive iterations in this algorithm. The relatively higher intensity of pixel k than those of its nearest neighbors is reflected by a positive value of the derivative and hence a larger value of the denominator. As a consequence, the penalty for the next iteration of mk(nþ1) is higher, resulting in a lower estimate for the intensity of pixel k. On the other hand, the intensity of pixel k lower than those of its nearest neighbors results in a higher estimate of the intensity of pixel k. Figure 23.4.2b shows that the derivative of the sigmoidal function exhibits a band of high (0 –0.05) and low (20.05– 0) values dependent on the parameter j. Because of this characteristic, the penalty imposed is effective only when intensity differences fall into this band. As a consequence, the unusually high differences in intensity such as sharp-edged features of the image are preserved while the speckled appearance of the image due to Poisson noise is smoothed. As shown in Fig. 23.4.2, the degree of edge separation needed can be adjusted with the parameter j for any particular image reconstruction.
676
Probabilistic Methods in Transmission Tomography
FIGURE 23.4.2
The derivative of the lncosh function, on the other hand, monotonically increases. As a consequence, there is no discrimination between edges and artifacts, resulting in uniformly smoothed edges. Hence the sigmoidal potential function should give a better reconstruction of the images.
B 23.5
COMPUTER SIMULATION
A computer simulation was performed to investigate the quality of reconstructed images using the sigmoid potential function and to compare it with lncosh potential function. Comparisons are also made with the conventional convolution backprojection (CBP) and the straight expectation – maximization algorithm. The simulation block diagram is shown in Fig. 23.5.1a. The simulation to obtain the four images for comparison is shown in Fig. 23.5.1b. A phantom was created with parameters as shown in Fig. 23.5.2. The numerical values show the uniform attenuation coefficients in that region. This phantom is fairly complicated in detail, including a high-contrast outer shielding ring. In the usual reconstructions, especially from projections with a low number of photon counts, this shielding ring obscures the delicate features present inside it. The intention in this study was to assess how well the sigmoidal potential function handled the presence of the shielding ring. The photon source was assumed to emit a relatively low number of di ¼ 104 expected number of photons per projection path i. The phantom was divided into cells of 64 64 pixels. A major problem was in the implementation of the coefficient matrix [lij] with a size of 642 642 . [lij] is a collection of lengths of the path along the projection path i inside pixel j. This is a highly sparse matrix, as each path i passes through (Fig. 23.2.1) only a small fraction of the total number of pixels in the image, and each pixel has only a small fraction of the total number of paths passing through it. Furthermore, computation for each projection path i requires access to all corresponding [lij]. Even though only a fraction of elements of this matrix is nonzero, calculation of all of them on the fly for each iteration would still require an excessive amount of computation. To reduce this computational burden, the pathlengths are calculated only once and stored in a form that is easily accessible. These stored data are defined as the pathlength matrix. A semisparse matrix scheme is devised to handle the problem of computational burden. Two sets of path coefficients are stored. The first set is accessible in the
23.5
Computer Simulation
677
FIGURE 23.5.1
“forward” direction; that is, given the path i, all coefficients associated with it are stored. This is defined as the forward pathlength matrix. The second set is accessible in the “backward” direction; that is, given pixel j, all coefficients associated with it are stored. This is defined as the backward pathlength matrix. The forward pathlength matrix is used to generate noise-free projection data. This matrix, in conjunction with a Poisson random-number generator routine, is used to
FIGURE 23.5.2
678
Probabilistic Methods in Transmission Tomography
generate Poisson-randomized data. The Poisson random number is generated for a given intensity parameter and acquisition time. This random number is equal to a total number of events that have exponentially P distributed interarrival times with mean equal to the given m21 intensity parameter gim ¼ die 2 k¼1 mklik occurring during the acquisition time. Exponential random variates are generated from a set of random variates uniformly distributed over [0,1] by probability transformation discussed in Chapter 15. The data from the Poisson randomizer are the projection matrix Y. From this projection matrix Y four different types of reconstruction images are generated. The first of the reconstructed images used the conventional deterministic convolution backprojection algorithm. In the other three images using the EM algorithm, a combination of forward and backward pathlength matrices are used in reconstructing the image. Both Nik and N^ ik defined in Eq. (23.3.3) are computed by using the forward pathlength matrix. The computed Nik and N^ ik are then in turn utilized to determine the updated estimated attenuation coefficient m(nþ1) in Eqs. (23.3.7) and (23.3.11) using the backward pathlength matrix. k These three images are reconstructed using (1) the conventional EM algorithm proposed by Lange [12], (2) the modified EM with lncosh potential function proposed by Green [9], and (3) the modified EM with the proposed sigmoidal potential function. Performances of these algorithms and their reconstructions are then compared. The criteria used for comparison are
.
Convergence and convergence rates Image quality (primarily visual tempered with analytical criteria)
.
Complexity (actual execution time)
.
Noise-free projection data were generated over 64 equally spaced projection angles between 0 and 1808 using parallel beamscanner geometry. Each projection consisted of 64 equally spaced rays over the field of view of 30 cm in length. Poisson-randomized projection data were generated using the previous noise-free data as the mean value. The Poisson random-number generator algorithm as described previously is used for this purpose. The CBP method was implemented on an Apollo Domain series 3500 system. All EM-based algorithms were implemented on the Alliant FX-2800 series system, which has both concurrent and vector processing capability. Each iteration took about 18 s. This may be slow but is not deemed a serious drawback because the speed of iteration can be considerably enhanced with multiprocessor machines. The grayscale images presented herein are windowed between 0.150 and 0.275 cm21 for better visual contrast.
B 23.6
RESULTS AND CONCLUSIONS
Figure 23.6.1 shows (a) the original phantom image, (b) reconstruction with the convolution backprojection (CBP), (c) reconstruction with the conventional EM, (d) reconstruction with EM-OSL using lncosh potential function, and (e) reconstruction with EM-OSL using sigmoidal potential function. Cross-sectional histograms of reconstructed images sectioned vertically through the center with the cross-sectional histogram of the phantom superimposed are shown in Fig. 23.6.2. In Fig. 23.6.3, the progression of RMS errors between the reconstructed images and the phantom as the iteration proceeds are shown for CBP, EM, lncosh, and sigmoid realizations. The conventional convolution backprojection (CBP) reconstruction shown in Fig. 23.6.1b is achieved in a single step. It is the quickest in execution time because of
23.6
Results and Conclusions
679
FIGURE 23.6.1
its deterministic nature. However, it is the poorest reconstruction among all the four. This poor performance is caused by factors such as intrinsic statistical variation of data, low photon counts, low angular and spatial sampling rates, and shielding ring effects. The cross-sectional histogram of Fig. 23.6.2a shows well-rounded edges, and the RMS error shown in Fig. 23.6.3 is quite high. By using a probabilistic model, better results are obtained for the EM algorithms as shown in Figs. 23.6.1c – 23.6.1e. However, the straight EM algorithm in Fig. 23.6.1c has a grainy and speckled appearance. This graininess is simply a manifestation of low correlation between parameter estimates for neighboring pixels. The cross-sectional histogram in Fig. 23.6.2b shows severely jagged edges, and the RMS error shown in Fig. 23.6.3 remains fairly constant after about 60 iterations. There is also the problem that this algorithm tends to diverge [13] after a certain number of iterations, and stopping rules are to be devised for the optimum number of iterations. To smooth the speckles in the EM procedure, the algorithm was modified using Gibbs’ prior with a lncosh potential function to penalize large differences between
680
Probabilistic Methods in Transmission Tomography
FIGURE 23.6.2
adjacent pixels. The lncosh reconstruction of Fig. 23.6.1d shows a smoother result, but the cross-sectional histogram of Fig. 23.6.2c indicates smoothed edges. The RMS error curve of Fig. 23.6.3 is also better than CBP and EM reconstructions but tends to saturate at a slightly higher error level. The difficulty stems from the fact that the lncosh potential function tends to oversmooth the image, resulting in the loss of sharp-edged features.
FIGURE 23.6.3
23.8
References for Chapter 23
681
To overcome the oversmoothing difficulty, a sigmoidal potential function that preserves sharp edges while retaining the smoothing effect is proposed. Convergence curves for increasing iterations were plotted using the loglikelihood function and RMS error for different values of b and j; b was varied between 0 and 0.0004, and j was varied between 0 and 7000. From these curves the optimum value of b was found to be 0.0002 and the optimum value of j was 5000. With these values, the reconstructed image of Fig. 23.6.1e is superior in quality to the rest. The image is visually very close to the phantom. The cross-sectional histogram of Fig. 23.6.2d also indicates a better edge performance than the lncosh potential function. The RMS error curve monotonically decreases as the iteration proceeds and at the 120th iteration is almost negligible. Thus, the modified EM algorithm with the sigmoidal potential function clearly performs better with regard to accuracy and smoothness of reconstruction.
B 23.7
DISCUSSION OF RESULTS
In comparing reconstructions in medical imaging, visual accuracy is the most important. Analytical criteria such as root-mean-square (RMS) error is secondary. The RMS error curves of Fig. 23.6.3 indicate major differences between the CBP reconstruction and EM reconstructions. The visual inspection of the reconstructed images also show discernible differences. On the other hand, the error curves for EM and lncosh reconstructions are not that far apart; but visually the differences are quite discernible. The sigmoidal reconstruction is clearly more appealing both visually and analytically. The error curves also show that during the first 30 iterations all the EM algorithms converge almost linearly at the same rate. But after that, their convergence becomes sublinear and their graphs begin to fan out separately. Among all the four convergence rates presented, the sigmoidal algorithm again performs the best. The speed of each iteration is dependent on the number of processors in the computer and can be considerably enhanced with more processors. The structure of this algorithm is more conducive to a multiprocessor machine. The convergence rates and the quality of reconstruction indicate the decided superiority of this modified EM algorithm using sigmoidal potential functions.
B 23.8
REFERENCES FOR CHAPTER 23
1. Rockmore, A. J., and A. Macovski, A maximum likelihood approach to emission image reconstruction from projections, IEEE Trans. Nucl. Sci. NS-23, 1428– 1432 (1976). 2. Rockmore, A. J., and A. Macovski, A maximum likelihood approach to transmission image reconstruction from projections, IEEE Trans. Nucl. Sci. NS-24, 1929– 1935 (1977). 3. Dempster, A. P., N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. B 39, 1 – 38 (1977). 4. Shepp, L. A., and Y. Vardi, Maximum likelihood reconstruction for emission tomography, IEEE Trans. Med. Imag. MI-1, 113– 121 (1982). 5. Lange, K., and R. Carson, EM reconstruction algorithms for emission and transmission tomography, J. Comput. Assist. Tomogr. 8(2), 306 –316 (1984). 6. Geman, S., and D. E. McClure, Bayesian image analysis: An application to single photon emission tomography, Proc. Am. Statist. Assoc. Statist. Comput. 12 – 18 (1985). 7. Geman, S., and D. E. McClure, Statistical methods for tomographic image reconstruction, Bull. Int. Statist. Inst. LII-4, 5 – 21 (1987).
682
Probabilistic Methods in Transmission Tomography
8. Hebert, T., and R. Leahy, A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs’ priors, IEEE Trans. Med. Imag. 8(2), 194 – 202 (1989). 9. Green, P. J., Bayesian reconstructions from emission tomography data using a modified EM algorithm, IEEE Trans. Med. Imag. 9(1), 84 – 93 (1990). 10. Geman, S., and D. Geman, Stochastic relaxation, Gibbs’ distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell. PAMI-6, 721 – 741 (1984). 11. Besag, J., Spatial interaction and the statistical analysis of lattice system, J. Roy. Statist. Soc. B 36, 192– 236 (1974). 12. Lange, K., Convergence of EM image reconstruction algorithms with Gibbs’ smoothing, IEEE Trans. Med. Imag. 9(4), 439– 446 (1990). 13. Rajeevan, N., A Stochastic Estimation Approach to Emission Tomography, Ph.D. thesis, Electrical Engineering Dept., Indian Institute of Science, Bangalore, India, 1991. 14. Kamonpet, P., and V. Krishnan, Modified EM Algorithm for Transmission Tomography, Internal Memo T94-1, Center for Advanced computation, Univ. Massachusetts Lowell, 1994.
APPENDIX
A
A Fourier Transform Tables
The governing equations are as follows: ð1 Fourier transform:
x(t)ejvt dt
X(v) ¼ 1
Inverse Fourier transform:
x(t) ¼
1 2p
ð1
X(v)e jvt dv
1
Tables give, in the following order, the time function x(t), the plot of the time function, the plot of the Fourier transform, and the frequency function X(v).
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
683
684
Appendix A
APPENDIX A Fourier transform tables
(Continued)
Appendix A
APPENDIX A
685
Continued
(Continued)
686
Appendix A
APPENDIX A Continued
APPENDIX
B
Cumulative Gaussian Tables
The governing equations are as follows: Gaussian density: Gaussian distribution function:
1 2 f (x) ¼ pffiffiffiffiffiffi e(1=2)x 2p ðx 2 1 pffiffiffiffiffiffi e(1=2)j dj ¼ p F(x) ¼ 2p 1
Tables give the values of the probability p ¼ F(x) for values of x between 25.00 and þ5.00. Figure B.1 shows the standard Gaussian density and distribution with x along the horizontal axis (abscissa) and f (x), F(x) ¼ p along the vertical axis (ordinate). Representative point is shown for x ¼ 0.5744 and F(0:5744) ¼ 0:7:
FIGURE B.1
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
687
688
Appendix B
APPENDIX B
Cumulative Gaussian tables
(Continued)
Appendix B
APPENDIX B
689
Continued
(Continued)
690
Appendix B
APPENDIX B
Continued
(Continued)
Appendix B
APPENDIX B
Continued
691
APPENDIX
C
Inverse Cumulative Gaussian Tables
The governing equations are as follows: ðx 2 1 pffiffiffiffiffiffi e(1=2)j dj ¼ p and F(x) ¼ 2p 1
x ¼ F1 (p)
Tables give the values of x in x ¼ F1 ( p) for values of p between (0.01, 0.99) grouped in such a manner that adjacent column values of p add to 1. Figure C.1 shows f (x), p along the horizontal axis and x, F1 ( p) along the vertical axis. Since f (x) is an even function, F1 ( p) has the symmetry F1 ( p) ¼ F1 (1 p). Representative points are shown for p ¼ 0.1 and 0.9 with F1 (0:1) ¼ 1:2816 and F1 (0:9) ¼ 1:2816.
FIGURE C.1
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
692
Appendix C
APPENDIX C
Inverse cumulative Gaussian tables
693
APPENDIX
D
Inverse Chi-Square Tables
The governing equations are as follows: Chi-square density:
Chi-square distribution function:
x(n=2)1 e(x=2) n u(x) 2n=2 G 2 ð x (n=2)1 (j=2) j e n dj ¼ p F(x) ¼ 0 2n=2 G 2
f (x) ¼
Tables give the values of x in x ¼ F 21( p) for values of p between (0.005, 0.995) grouped in such a manner that adjacent column values of p add to 1. Figure D.1 shows f (x), p along the horizontal axis and x, F 21( p) along the vertical axis. Representative points are shown for number of degrees of freedom n ¼ 4, and p ¼ 0.1 and 0.9 with F 21(0.1) ¼ 1.06362 and F 21(0.9) ¼ 7.7794. The asymmetry of the distributions is to be noted and F 1( p) = F 1(1 p).
FIGURE D.1
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
694
Appendix D
695
APPENDIX D Inverse chi-square tables
(Continued)
696
Appendix D
APPENDIX D Continued
(Continued)
Appendix D
APPENDIX D Continued
(Continued)
697
698
Appendix D
APPENDIX D Continued
(Continued)
Appendix D
699
APPENDIX D Continued
(Continued)
700
Appendix D
APPENDIX D Continued
APPENDIX
E
Inverse Student-t Tables
The governing equations are as follows: nþ1 ½(nþ1)=2 G t2 2 Student-t density: f (t) ¼ pffiffiffiffiffiffi n 1 þ n np G 2 nþ1 ½(nþ1)=2 ðt G t2 2 Student-t distribution function: F(t) ¼ dt pffiffiffiffiffiffi n 1 þ n 1 npG 2 Since f (x) is an even function, F 21( p) has the symmetry F 1 ( p) ¼ F 1 (p). The absolute values of t for p between (0.005,0.45) are the same as the values for p between (0.55, 0.999) with t ¼ F 1 (0:5) ¼ 0 for all n. Hence the tables give the values of t ¼ F 1 ( p) for p only between (0.55, 0.999). Figure E.1 shows f (t), p along the horizontal axis and t, F 1 ( p) along the vertical axis. Representative points are shown for number of degrees of freedom n ¼ 5, and p ¼ 0.1 and 0.9 with F 1 (0:1) ¼ 1:4759 and F 1 (0:9) ¼ 1:4759.
FIGURE E.1
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
701
702 APPENDIX E Inverse student-t tables
(Continued)
Appendix E
APPENDIX E Continued
703
APPENDIX
F
Cumulative Poisson Distribution
The governing equations are as Follows: Poisson mass function: p(k; l) ¼ el
Poisson distribution function:
F(k; l) ¼
lk , k!
k X m¼0
el
k0 lm , k0 m!
Tables give the values of the probability F(k;l) for values of l between (0.01, 10) and k between (0,20). Figure F.1 shows the Poisson mass function and distribution with k along the horizontal axis and p(k;l), F(k;l) along the vertical axis for l ¼ 10. The representative point is shown for k ¼ 10 and p(k;10) ¼ 0.1251, F(k;10) ¼ 0.5830.
FIGURE F.1
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
704
Appendix F
705
APPENDIX F Cumulative Poisson distribution
(Continued)
706
Appendix F
APPENDIX F Continued
(Continued)
Appendix F
APPENDIX F Continued
707
APPENDIX
G
Cumulative Binomial Distribution
The governing equations are as follows: Binomial mass function:
b(k; n, p) ¼
n! k!(n k)! pk (1 p)nk ,
Binomial distribution function:
F(k; n, p) ¼
0kn
k X
n! m!(n m)! m¼0 pm (1 p)nm ,
0kn
Tables give the values of the probability F(k; n, p) for values of p between (0.05, 0.9) and n between (2, 20). Figure G.1 shows the binomial mass function and distribution with k along the horizontal axis and b(k; n, p), F(k; n, p) along the vertical axis for p ¼ 0.3 and n ¼ 10. The representative point is shown for k ¼ 3 and b(k; 10, 0.3) = 0.2668, F (k; 10, 0.3) ¼ 0.6496. The range of k is only upto n.
FIGURE G.1 Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
708
Appendix G
APPENDIX G
709
Cumulative binomial distribution
(Continued)
710
Appendix G
APPENDIX G
Continued
(Continued)
Appendix G
APPENDIX G
711
Continued
(Continued)
712
Appendix G
APPENDIX G
Continued
(Continued)
Appendix G
APPENDIX G
Continued
713
REFERENCES
1. Abromowitz, M., and I. A. Stegun, Handbook of Mathematical Functions, Dover Publications, 1972. 2. Akansu, A. N., and R. A. Haddad, Multiresolution Signal Processing, Academic Press, 1992. 3. Anderson, R. L., Distribution of the serial correlation coefficient, Annals Math. Statist., 13, 1– 13, 1942. 4. Aunon, J. I., and V. Chandrasekhar, Introduction to Probability and Random Processes, Mc-Graw-Hill, 1997. 5. Benford, F., The law of anomalous numbers, Proc. Am. Phil. Soc., 78, 551– 572, 1938. 6. Bharucha-Reid, A. T., Elements of the Theory of Markov Processes and Their Applications, Dover Publications, 1988. 7. Black, F., and M. Scholes, The pricing of options and corporate liabilities, J. Polit. Econ., 1973. 8. Bo¨rjesson, P. O., and C. E. W. Sundberg, Simple approximations of the error function Q(x) for communications applications, IEEE Trans. Commun., 27, 639– 643, 1979. 9. Box, G. E. P., G. M. Jenkins, and G. C. Reinsel, Time Series Analysis, Prentice-Hall, 1994. 10. Chatfield, C., The Analysis of Time Series: An Introduction, 2nd ed., Chapman & Hall, 1980. 11. Childers, D. G., Probability and Random Processes, Irwin, 1997. 12. Cooper, G. R., and C. D. McGillem, Probabilistic Methods of Signal and System Analysis, 3rd ed., Oxford Univ. Press, 1999. 13. Cox, D. R., and H. D. Miller, The Theory of Stochastic Processes, Chapman & Hall/CRC, 1977. 14. Davenport, W. B., and W. L. Root, An Introduction to the Theory of Random Signals and Noise, IEEE Press, 1987.
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
714
15. Davis, J., Performance Models for Correlated Traffic Data, Ph.D. thesis, Univ. Massachusetts—Lowell, 2000. 16. Doob, J. L., Stochastic Processes, Wiley, 1953. 17. Feller, W., An Introduction to Probability Theory and Its Applications, 2nd ed., Wiley, 1968, Vol. I. 18. Fogiel, M., Handbook of Mathematical, Scientific, and Engineering, Formulas, Tables, Functions, Graphs, Transforms, Staff of Research and Education Assoc., 1988. 19. Fujisaki, M., G. Kallinapur, and H. Kunita, Stochastic differential equations for nonlinear filtering problem, Osaka J. Math., 9, 19 – 40, 1972. 20. Gantmacher, F., The Theory of Matrices, Chelsea Publishing, 1959. 21. Gardner, W. A., Introduction to Random Processes, 2nd ed., Mc-Graw-Hill, 1990. 22. Grimmett G., and D. Stirzaker, Probability and Random Processes, 3rd ed., Oxford Univ. Press, 2001. 23. Gross, D., and C. M. Harris, Queuing Theory, Wiley, 1998. 24. Higgns, J. J., and S. Keller-McNulty, Concepts in Probability and Stochastic Modeling, Duxbury Press, 1995. 25. Jenkins, G. M., and D. G. Watts, Spectral Analysis and Its Applications, Holden-Day, 1968. 26. Kallianpur, G., Stochastic Filtering Theory, Springer-Verlag, 1980. 27. Kalman, R. E., and R. S. Bucy, New results in linear filtering and prediction theory, Trans. ASME, J. Basic Eng., ser. D, 83, 95 – 108, 1961. 28. Kamonpet, P., and V. Krishnan, Modified EM Algorithm for Transmission Tomography, Internal Memo T94-1, Center for Advanced Computation, Univ. Massachusetts Lowell, 1994.
References
29. Kamonpet, P., Probabilistic Image Reconstruction from Noisy and Incomplete Projections, Ph.D. thesis, Univ. Massachusetts—Lowell, 1995. 30. Kleinrock, L., Queuing Systems, Theory, Wiley, 1975, Vol. II. 31. Kleinrock, L., Queuing Systems, Computer Applications, John Wiley, 1976, Vol. II. 32. Krishnan, V., Estimation of Autocovariance Functions, Technical Report M-215U, Dynamics Research Corporation, 1974. 33. Krishnan, V., Confidence Limits and Equivalence of Statistical Tests, Technical Report M-256U, Dynamics Research Corporation, 1976. 34. Krishnan, V., Distribution of Spectral Estimators, Technical Report E-9334U, Dynamics Research Corporation, 1984. 35. Krishnan, V., Nonlinear Filtering and Smoothing, Wiley, 1984. 36. Krishnan, V., Linear Systems Properties, CRC Press, 1998. 37. Krishnan, V., Nonlinear Filtering and Smoothing, Dover Publications, 2005. 38. Lange, K., Convergence of EM image reconstruction algorithms with Gibbs’ smoothing, IEEE Trans. Med. Imag., 9, 439– 446, 1990. 39. Leon-Garcia, A., Probability and Random Processes for Electrical Engineering, AddisonWesley, 1993. 40. Lipster, R. S., and A. N. Shiryaev, Statistics of Random Processes, Springer-Verlag, 1977, Vols. I and II. 41. Meyer, P. A., A decomposition theorem for supermartingales, Ill. J. Math. 6, 193– 205, 1962. 42. Mood, A. M., and F. A. Graybill, Introduction to the Theory of Statistics, McGraw-Hill, 1963. 43. Naidu, P. S. Modern Spectrum Analysis of Time Series, CRC Press, 1996. 44. Ostle, B., Statistics in Research, Iowa State Univ. Press, 1963. 45. Papoulis, A., Fourier Integral and Its Applications, Mc-Graw Hill, 1962. 46. Papoulis, A., Probability, Random Variables, and Stochastic Processes, 3rd ed., McGrawHill, 1991. 47. Peebles Jr., Peyton Z., Probability, Random Variables and Random Signal Principles, 4th ed., Mc-Graw-Hill, 2001. 48. Priestley, M. B., Spectral Analysis and Time Series, Academic Press, 1981.
715
49. Proakis, J. G., Digital Communications, McGraw-Hill, 1995. 50. Rice, S. O., Mathematical analysis of random noise, in Selected Papers on Noise and Stochastic Processes, N. Wax, ed., Dover Publications, 1945, 1954. 51. Rome, H. J., and V. Krishnan, Causal probability model for evaluating future transoceanic airlane separations, IEEE Trans. Aerospace Electron. Syst., 26, 804 – 817, 1990. 52. Ross, S. M., A First Course in Probability, 6th ed., Prentice-Hall, 2002. 53. Ross, S. M., Stochastic Processes, 2nd ed., John-Wiley, 1996. 54. Ross, S. M., Introduction to Probability Models, 7th ed., Academic Press, 2000. 55. Shanmugan, S., and A. M. Breiphol, Random Signals, Detection, Estimation and Data Analysis, Wiley, 1988. 56. Shilov, G. E., An Introduction to the Theory of Linear Spaces, Prentice-Hall, 1961. 57. Srinath, M. D., P. K. Rajasekaran, and R. Viswanathan, Introduction to Statistical Signal Processing with Applications, Prentice-Hall, 1996. 58. Stark, H., and J. W. Woods, Probability and Random Processes with Applications to Signal Processing, 3rd ed., Prentice-Hall, 2002. 59. Therrien, C. W., Discrete Random Signals and Statistical Signal Processing, Prentice-Hall, 1992. 60. Thomas, J. B., An Introduction to Communication Theory and Systems, Springer-Verlag, 1988. 61. Trivedi, K. S., Probability and Statistics with Reliability, Queuing and Computer Science Applications, Prentice-Hall, 1982. 62. Viniotis, Y., Probability and Random Processes for Electrical Engineers, McGraw-Hill, 1998. 63. Xu, L., Probabilistic Reconstruction of Tomographic Images, Ph.D. thesis, Univ. Massachusetts—Lowell, 1997. 64. Yates, R. D., and D. J. Goodman, Probability and Stochastic Processes, Wiley, 1999.
Web References W1. http://www.itl.nist.gov/div898/handbook/ eda/section3/eda36.htm. W2. http://www.geocities.com/mikemclaughlin/ math_stat/Dists/Compendium.html.
Index
acceptance –rejecztion, 280 algorithm, 281 beta density, 282 comparison function, 280 adaptive coefficients, 307 aperiodic random processes, 563 discrete Karhunen – Loeve (K– L) expansion, 571 Karhunen – Loeve (K– L) expansion, 565 eigenfunctions, 565 eigenvalues, 565 nonstationary process, 566 AR(1) process, 522 arcsine law, 192, 194 attenuation coefficient, 668 autoregressive process, 439 bandpass signals, 606 analytic signal representation, 607 natural envelope, 606 lower side band, 607 upper side band, 606 complex envelope, 610 quadrature representation, 607 Bayesian estimation, 384 discrete probability, 398 MAP Criterion, 395 ML criterion, 396 Bayes’ theorem, 20, 669 Bayes’ theorem for densities, 132 Bernoulli distribution, 37 trials, 37 independent identically distributed, 37 Bernoulli process, 505 modified, 510 Bessel function of zero order modified, 107, 217, 621 bilinear form, 302 binomial coefficient, 29
Pascal’s identity, 29 triangle, 30 properties, 29 sum, 39 Vandermonde’s identity, 30 binomial process, 506, 509 birthdays problem, 27, 53 bivariate Gaussian distribution, 144, 154 block matrices, 308 properties, 309 Bose Einstein statistics, 32 bound, 242 Chebyshev, 242 alternate forms, 244 geometric derivation, 243 Chernoff, 248 Brownian motion (see Wiener process), 492, 523 Catalan numbers, 31 Cauchy density, 232 distribution, 115, 221, 226 Cauchy – Riemann conditions, 303 Cayley Hamilton theorem, 294 central limit theorem, 260 characteristic functions, 155, 212, 213 examples, 157 continuous random variables, 159 discrete random variables, 157 existence, 155 joint characteristic functions, 157 moment generating properties, 156 chi-squared test, 267 combinations, 28 sampling with replacement, 32 sampling without replacement, 29 comparison of Gaussian and Poisson approximations, 93 conditional densities, 122
Probability and Random Processes, by Venkatarama Krishnan Copyright # 2006 John Wiley & Sons, Inc.
716
Index
conditional distributions, 122 properties, 122 conditional distributions and densities, 122, 313 clarification, 315 P(A) ¼ 0, 126 P(A) = 0, 122 confidence coefficient, 351, 364 confidence interval, 351, 364 coefficients of simple linear regression, 371 unknown mean of population, 364 known variance, 364 unknown variance, 366 unknown variance with unknown mean, 369 confidence region simple linear regression line, 372 congruential generator, 264 linear, 264 mixed, 264 multiplicative, 264 continuous distributions, 79, 95 beta distribution, 114 Cauchy distribution, 115 chi distribution, 104 Maxwell density, 106 Nakagami density, 108 Rayleigh density, 105 Rice’s density, 107 chi-square, 102 double Laplace distribution, 118 Erlang distribution, 97 exponential distribution, 80 hazard rate, 82 instantaneous, 82 Poisson arrival process, 81 gamma, 99 reproductive property, 100 Gaussian distribution, 84 Gibbs distribution, 118, 674 potential function, 118 Laplace distribution, 96 double Laplace distribution, 118 lognormal distribution, 112 Pareto distribution, 117 Snedecor F-distribution, 111 Student-t density, 110 summary, 119 triangular, 95 uniform distribution, 79 Weibull, 101 continuous Kalman filter, 660 assumptions, 660
717
matrix Riccati equation, 661 continuous random variables, 151 means and variances, 150 exponential distribution, 151 Gaussian distribution, 152 convergence, 256 almost sure, 257 Cauchy criterion, 257 in distribution, 258 in probability, 257 mean square, 258 pointwise, 256 properties, 258 quadratic mean, 258 convolution techniques, 279 Erlang distribution, 279 triangular distribution, 279 counting, 25 addition rule, 25 multiplication rule, 25 convex function, 254 covariance matrices, 320 diagonalization principles, 330 diagonalization to an identity matrix, 331 principles, 330 simultaneous diagonalization, 334 summary of procedures, 335 covariance of ACF estimators, 452 craps game, 16 cumulants, 167 Gaussian distribution, 169 Poisson distribution, 169 cumulant generating functions, 167 cyclostationary process, 560 density function, 65 differentiation, 301 complex variable, 303 Cauchy – Riemann conditions, 303 partial derivative, 304 regular point, 304 singular point, 304 total derivative, 303 complex vectors, 305 partial differentiation, 305 real matrices, 308 real vectors, 301 direct determination of density, 194 conditions, 196 steps, 196 direct determination of joint density, 227 discrete distributions Bernoulli, 37 Benford, 55
718
Index
discrete distributions (Continued) binomial, 38 approximation, 50 geometric, 42 hypergeometric, 46 logarithmic, 55 finite law, 55 first significant digit, 55 second significant digit, 58 infinite law, 60 multinomial, 41 negative binomial, 44 Pascal distribution, 44 Poisson, 48 approximation to binomial, 50 summary, 62 discrete Kalman filter, 647 across observations, 649, 651 algorithm, 655 assumptions, 650 between observations, 649, 652 comments, 654 covariance propagation, 655 Kalman gain, 650 scalar case, 657 timing diagram, 649, 653 discrete random variables, 150 means and variances, 150 binomial distribution, 150 Poisson distribution, 151 discrete time processes, 458 Anderson’s theorem, 470 estimation of autocorrelation functions, 467 estimation of covariance of NACF, 469 estimation of NACF, 468 large-lag approximations, 469 variance of autocovariance estimators, 464 white noise, 470 discrete time stationary processes, 437 moments, 437 distribution and density functions, 66 properties, 73 cumulative distribution function FX (x), 73 probability density function fX (x), 74 probability mass function, 74 distribution function, 66 from density, 75 distribution of Y ¼ g(X ), 174 steps for determination, 175 eigenvalues of matrices, 294 characteristic polynomial, 293
eigenvectors of matrices, 296 normalization, 297 emission tomography, 667 ergodic processes, 439 correlation-ergodic, 441 mean-ergodic, 439 power ergodic, 442 time average, 439 estimated regression coefficients, 358 covariance, 359 from data, 358 estimated regression line, 360 variance, 360 estimation likelihood function, 389 likelihood ratio, 389 linear estimation of vector variables, 337 overdetermined system, 338 underdetermined system, 338 maximum a posteriori probability (MAP), 388 minimization of average probability of error, 389 maximum likelihood, ML, 391 properties, 391 parameters, 350 Bayesian estimation, 384 covariance, 355 higher order moments, 358 interval estimation, 351 mean, 351 unbiased, 351 minimum mean square, 353 point estimation, 351 variance, 354 standard error, 355 statistic, 351 estimation criteria, 340 consistent, 341 efficient, 341 Rao – Cramer bound, 341 log-likelihood, 341 maximum likelihood, 341 mean square error, 340 minimum variance, 340 unbiased, 340 estimation of a random variable by a constant, 342 by a function of another random variable, 342 expectation maximization algorithm, 672 one step late, 672 RMS error, 680
Index
field, 8, 11 Boolean field, 9 Borel s-field, 9 functions of a single random variable, 173 higher order moments, 203 moments, 202 random variable g(X ), 173 conditions, 173 function of two random variables, 206 image, 206 inverse image, 206 conditions, 206 steps for determining FZ(z), 207 gambler’s ruin, 518 gamma function, 99 incomplete, 100 Gaussian approximation to binomial distribution, 91 Demoivre – Laplace limit theorem, 91 Gaussian distribution, 84 density function, 84 distribution function, 84 mean, 84 standard, 85 properties, 87 tails function Q(x), 88 lower bound, 89 properties, 90 upper bound, 89 variance, 84 Gaussian process, 521 properties, 521 generating functions, 161 examples, 162 binomial distribution, 163 geometric distribution, 164 Poisson distribution, 163 moment generating properties, 162 Gibbs’ distribution, 118, 674 potential function, 674 lncosh, 674 sigmoid, 675 Gibbs’ prior, 667 higher order moments, 153 cross moments, 154 Hilbert transform, 607 table, 610 histograms, 266 hypothesis testing, 373 acceptance region, 374
alternate hypothesis, 373 binary, 375 composite hypothesis, 374 cost for errors, 401 mean m—known variance s2, 378 mean m—unknown variance s2, 379 ML rule, 394 number of samples n for given a and b, 382 null hypothesis, 373 operating characteristic, 376 power of the test, 376 procedure for testing, 378 rejection region, 374 significance testing, 373, 374 simple hypothesis, 374 statistic, 373 image, 64 independence, 4, 18 functional, 4, 18 statistical, 4, 18 independent increment process, 507 stationary independent increment, 507 indicator function, 205, 243 properties, 205 inequalities, 242 Cauchy – Schwartz, 251 frequency domain, 253 Chebyshev, 242 geometric derivation, 243 generalized Chebyshev, 244 Jensen, 254 Markov, 246 innovations process, 648 interval estimation, 364 inverse Hilbert transform, 607 inverse image, 64 inverse problem g(x) from fX (x) and fY ( y), 200 inverse transformation, 269 discrete distributions, 270 continuous distributions, 272 exponential distribution, 272 Rayleigh distribution, 274 Weibull distribution, 273 tables, 278 Jacobian determinant, 227, 230, 233 Jacobian matrix, 227, 230, 233 joint cumulants, 169 joint distribution, 135 continuous distribution, 136 discrete distribution, 135
719
720
Index
joint distribution (Continued) joint probability mass function, 135 marginal distribution, 135 properties, 137 region bounded by a function, 140 regions bounded by constants, 138 regions bounded by functions, 141 joint distribution of two vectors, 312 joint moments of two random vectors, 321 cross correlation matrix, 321 cross covariance matrix, 322 joint Gaussian density, 213 Karhunen – Loeve transform, 565 continuous, 565 discrete, 571 matrix formulation, 572 Kolmogorov axioms, 11 Boolean additivity, 11 non negativity, 11 normalization, 11 sigma additivity, 11 kurtosis, 168 Lagrange multipliers, 351 likelihood function, 341 log likelihood function, 341 limit theorems, 259 central limit theorem, 260 strong law of large numbers, 260 weak law of large numbers, 259 linear filters, 592 carrier frequency, 592 cut-off frequency, 592 ideal band-pass, 592 ideal high-pass, 592 ideal low-pass, 592 matched filters, 596 linear systems, 574 causal system, 574 convolution integral, 574 impulse response, 574 state transition matrix, 575 transfer function, 575 linear regression, 343 evaluating coefficients, 343 multiple, 343 simple, 343, 360 estimated regression line, 361 variance of regression coefficients, 362 linear transformations, 328 marginal distributions, 142, 143 Markov chains, 536
classification of states, 544 absorbing state, 544 aperiodic, 545 ergodic, 545 intercommunicating, 544 irreducible, 544 periodic, 545 recurrent, 545 reducible, 544 transient, 545 discrete, 536 first passage probability, 544 first return probability, 545 homogeneous, 536 mean first passage time, 545 mean recurrence time, 545 null recurrent, 545 positive recurrent, 545 m-step transition probability, 536 steady state, 539 properties, 550 state occupancy probabilities, 538 stochastic matrix, 537 Markov process, 527 AR process, 534 Chapman – Kolmogorov equation, 528 homogeneous, 529 Markov property, 527 Poisson process, 532 properties, 530 transition probability density, 528 Wiener process, 533 martingale process, 551 continuous martingale, 552 discrete martingale, 552 likelihood ratio martingale, 555 Poisson martingale, 554 random walk, 553 Wiener martingale, 554 Mass megabucks, 46 matched filters, 596 input signal to noise ratio, 597 non-white Gaussian noise, 604 output signal to noise ratio, 597 properties, 599 rectangular pulse, 600 sinusoidal pulse, 600 matrices, 284 addition, 285 adjoint, 291 characteristic equation, 293 characteristic polynomial, 293 definiteness, 295 negative definite, 295
Index
positive definite, 295 definition, 284 determinant, 286 properties, 287 diagonalization, 298 inverse, 290 properties, 292 multiplication, 285 orthogonal, 293 principal minors, 295 rank, 288 similarity transformation, 300 singular, 287 square, 284 symmetric, 299 properties, 299 trace, 288 transpose, 286 means and variances table, 170 means of estimated autocovariances, 449 minimum mean square estimator mean, 353 mixed distributions, 118 modal matrix, 297 modified Bessel function I0 (z), 107, 217, 621 modulation, 434 moment generating functions, 164 gamma distribution, 167 Gaussian distribution, 166 negative binomial, 165 Monty Hall problem, 22 multiple functions of random variables, 238 narrowband bandpass process, 612 analytic signal representation, 615 complex envelope, 615 natural envelope representation, 618 quadrature representation, 614 properties, 615 with additive sinusoid, 619 normal equations, 344 operating characteristic, 376 orthogonal increment processes, 508 orthogonality principle, 346 random processes, 626 random variables, 625 Paley– Wiener criterion, 593, 631 partial differentiation, 304 complex vectors, 305
real matrices, 308 real vectors, 301 periodic random processes, 557 stationary, 558 permutations, 26 sampling with replacement, 26 sampling without replacement, 26 Poisson process, 491, 492, 508 formal derivative, 503 generalized, 501 autocorrelation, 502 autocovariance, 503 mean, 502 interarrival time, 496 distribution, 496 waiting time, 496 distribution, 497 Poisson white noise, 504 Polya urn model, 534 power spectral density, 472 predictable process, 557 probability, 10 conditional, 14 reduced sample space, 14, 17 density, 13 distribution function, 13 measure, 11 space, 10 total, 20 probability mass function, 38, 74 pseudoinverse, 338 quadrature components properties, 615 quadratic error surface, 307 quadratic form, 302 random binary wave, 429 random number generator, 264 properties, 265 random processes, 406 bandpass processes, 606 classification, 490 continuous state continuous time, 491 continuous state discrete time, 491 discrete state continuous time, 491 discrete state discrete-time process, 490 definition, 406 distribution and density, 408 estimation of parameters, 445 continuous time processes, 445 autocovariance, 447 covariance, 452 variance, 454
721
722
Index
random processes (Continued) mean, 446 variance, 446 discrete time processes, 458 autocovariance, 459 estimation, 461 Lagrange multiplier, 459 mean, 458 higher order distributions, 414 independent, 416 means and variances, 408 normalized autocovariance, 416 normalized cross covariance, 416 orthogonal, 417 second order moments, 415 autocorrelation, 415 autocovariance, 415 cross correlation, 416, 435 cross covariance, 416 normalized autocovariance, 416 normalized cross-covariance, 416 second order process, 414 through linear systems, 578 cross spectral density, 580 output power spectral density, 580 threshold filter, 588 uncorrelated, 417 random telegraph wave, 432 random variable, 64 conditional moments, 146 conditional expectation, 148 conditional variance, 149 continuous, 79 correlation coefficient, 150 covariance, 150 definition, 64 degenerate, 241 discrete, 37 independent, 147 distribution and density, 66 properties, 73 moments, 146 expectation, 146 properties, 147 joint, 148 variance, 149 properties, 149 orthogonal, 147 uncorrelated, 147 random vectors, 311 Bayes theorem, 316 conditional density, 328 inner product, 320 independent, 323 joint density, 313, 326
moments, 319 correlation matrix, 319 properties, 324 covariance matrix, 320 properties, 324 expectation vector, 319 orthogonal, 323 outer product, 320 uncorrelated, 323 random walk process, 490, 512 elementary, 509 symmetric, 510 two absorbing barriers, 514 unrestricted, 512 Rayleigh density, 215, 621 Rayleigh distribution, 215, 232, 619 recursive estimation, 648 regression coefficients, 343 Rice’s density, 217, 621 Rice’s distribution, 217, 621 sample space, 10 continuous, 10 discrete, 10 sample variance, 354 set, 1 cardinality, 2 unions and intersections, 5 Cartesian product, 7 complement, 6 De Morgan’s laws, 8 difference, 6 element, 1 empty set, 2 equal, 3 event, 9 finite, 1 inclusion – exclusion principle, 5 intersection, 4 mutually exclusive, 4 null, 11 partition, 7 power set, 3 relative complement, 6 subset, 2 symmetric difference, 7 table of set properties, 8 union, 3 universal, 2 Venn diagram, 2 shaping filter, 580 signal to noise ratio, 253, 597 significance level, 351, 364, 374 similarity transformations, 300 skewness, 168
Index
solution of linear equations, 291 special methods, 274 Box– Mueller transformation, 275 Gaussian distribution, 275 Rayleigh distribution with phase, 277 spectral density– continuous time, 472 bandlimited process, 475, 476 bandpass process, 477 cross spectral density, 473 power spectral density, 473 alternate form, 480 estimation, 482 periodogram, 480 properties, 479 white noise, 478 Wiener – Khinchine theorem, 472 spectral density– discrete time, 484 power spectral density, 484 alternate form, 486 discrete white noise, 488 spectral factorization, 633 additive factoring, 634 multiplicative factoring, 633 standard error, 355 stationary random processes, 420 autocorrelation, 421 autocovariance, 421 cross correlation, 422 cross covariance, 422 first order, 420 independent, 421 mean, 421 normalized autocovariance, 421 normalized cross covariance, 422 nth order stationary, 420 orthogonal, 421 periodic, 423 properties of correlation functions, 422 second order, 420 strict sense, 420 uncorrelated, 421 variance, 421 wide sense, 420 stochastic estimation algorithm, 671 expectation step, 671 maximization step, 672 strong law of large numbers, 260 submartingale, 552 decomposition, 557 supermartingale, 552 tables cumulative binomial, 708 cumulative Gaussian, 687
колхоз 6/22/06
cumulative Poisson, 704 Fourier transform, 683 inverse chi-square, 694 inverse Gaussian, 692 inverse Student-t, 701 tails probability, 242 total probability for densities, 131 transmission tomography computer simulation, 676 pathlength matrix, 676 backward, 677 forward, 677 phantom description, 676 probability model, 668 reconstructed images, 679 cross sectional histograms, 680 RMS error, 680 two functions of two random variables, 222 conditions, 223 auxiliary random variable, 233 derivation of Student-t, 237 type I error, 374 false alarm, 374 producer’s risk, 374 type II error, 374 consumer’s risk, 374 missed alarm, 374 uncorrelated increment process, 508 uniform distribution, 264 variance of the estimated regression line, 360 vector Gaussian random variables, 323 weak law of large numbers, 259 weighted Poisson process, 501 white Gaussian noise, 521 whitening filter, 632 white noise process, 422 Wiener filtering, 627 causal, 631 filtering, 627, 638 noncausal, 627 prediction, 627, 641 smoothing, 627, 642 Wiener– Hopf integral equation, 632 filtering solution, 634 prediction-smoothing solution, 637 Wiener process, 523 conditions, 523 derivation, 524 formal derivative, 526 properties, 526
723
