2nd Edition

Probability and Random Processes for Electrical and Computer Engineers

By Charles Therrien, Murali Tummala Copyright 2012
    432 Pages 143 B/W Illustrations
    by CRC Press

    With updates and enhancements to the incredibly successful first edition, Probability and Random Processes for Electrical and Computer Engineers, Second Edition retains the best aspects of the original but offers an even more potent introduction to probability and random variables and processes. Written in a clear, concise style that illustrates the subject’s relevance to a wide range of areas in engineering and physical and computer sciences, this text is organized into two parts. The first focuses on the probability model, random variables and transformations, and inequalities and limit theorems. The second deals with several types of random processes and queuing theory.

    New or Updated for the Second Edition:

    • A short new chapter on random vectors that adds some advanced new material and supports topics associated with discrete random processes
    • Reorganized chapters that further clarify topics such as random processes (including Markov and Poisson) and analysis in the time and frequency domain
    • A large collection of new MATLAB®-based problems and computer projects/assignments

    Each Chapter Contains at Least Two Computer Assignments

    Maintaining the simplified, intuitive style that proved effective the first time, this edition integrates corrections and improvements based on feedback from students and teachers. Focused on strengthening the reader’s grasp of underlying mathematical concepts, the book combines an abundance of practical applications, examples, and other tools to simplify unnecessarily difficult solutions to varying engineering problems in communications, signal processing, networks, and associated fields.

    Part I: Probability and Random Variables



    The Analysis of Random Experiments

    Probability in Electrical and Computer Engineering

    Outline of the Book

    The Probability Model

    The Algebra of Events

    Probability of Events

    Some Applications

    Conditional Probability and Bayes’ Rule

    More Applications

    Random Variables and Transformations

    Discrete Random Variables

    Some Common Discrete Probability Distributions

    Continuous Random Variable

    Some Common Continuous Probability Density Functions

    CDF and PDF for Discrete and Mixed Random Variables

    Transformation of Random Variables

    Distributions Conditioned on an Event


    Expectation, Moments, and Generating Functions

    Expectation of a Random Variable

    Moments of a Distribution

    Generating Functions

    Application: Entropy and Source Coding

    Two and More Random Variables

    Two Discrete Random Variables

    Two Continuous Random Variables

    Expectation and Correlation

    Gaussian Random Variables

    Multiple Random Variables

    Sums of Some Common Random Variables


    Inequalities, Limit Theorems, and Parameter Estimation


    Convergence and Limit Theorems

    Estimation of Parameters

    Maximum Likelihood Estimation

    Point Estimates and Confidence Intervals

    Application to Signal Estimation


    Random Vectors

    Random Vectors

    Analysis of Random Vectors


    Cross Correlation and Covariance

    Applications to Signal Processing



    Part II: Introduction to Random Processes

    Random Processes


    Characterizing a Random Process

    Some Discrete Random Processes

    Some Continuous Random Processes


    Random Signals in the Time Domain

    First and Second Moments of a Random Process

    Cross Correlation

    Complex Random Processes

    Discrete Random Processes

    Transformation by Linear Systems

    Some Applications


    Random Signals in the Frequency Domain

    Power Spectral Density Function

    White Noise

    Transformation by Linear Systems

    Discrete Random Signals



    Markov, Poisson, and Queueing Processes

    The Poisson Model

    Discrete-Time Markov Chains

    Continuous-Time Markov Chains

    Basic Queueing Theory



    A Basic Combinatorics

    B The Unit Impulse

    C The Error Function

    D Noise Sources


    Charles W. Therrien was born in Pittsfield, Massachusetts. He received both undergraduate and graduate (MS, Ph.D) degrees in electrical engineering from MIT. He was a member of the technical staff at Lincoln Laboratory from 1971 to 1984 and then moved to the Naval Postgraduate School in Monterey, California. During his more than 20 years there, he taught courses in systems and signal processing and authored three books, as well as numerous papers, in these areas.

    Murali Tummala is a professor in the department of electrical and computer engineering in the Graduate School of Engineering and Applied Sciences at the Naval Postgraduate School in Monterey, California. Since 1986, he has taught and conducted research there in the areas of computer networks and signal processing systems. He received his Ph.D from India Institute of Technology, Bombay in 1984.