The book's first five chapters form the basis of a traditional, introduction to probability and random variables. In addition to the standard topics, it offers optional sections on modeling, computer methods, combinatories, reliability, and entropy. Chapters 4 through 9 can accommodate a one-semester senior/first-year graduate course on random processes and linear systems, as well as Markov chains and queuing theory. Additional coverage includes cyclostationary random processes, Fourier series and Karhunen-Loeve expansion, continuity, derivatives and integrals, amplitude modulation. Wiener and Kalman filters, and time reversed Markov chains. Features Chapter overviews: brief introduction outlining chapter coverage and learning objectives. Chapter summaries: concise, easy-reference sections providing quick overviews of each chapter's major topics. Checklist of important terms. Annotated references: suggestions of timely resources for additional coverage of critical material. Numerous examples: a wide selection of fully worked-out real-world examples. Problems: over 700 in all. Ch. 1 Probability Models in Electrical and Computer Engineering 1 Ch. 2 Basic Concepts of Probability Theory 23 Ch. 3 Random Variables 84 Ch. 4 Multiple Random Variables 191 Ch. 5 Sums of Random Variables and Long-Term Averages 269 Ch. 6 Random Processes 329 Ch. 7 Analysis and Processing of Random Signals 403 Ch. 8 Markov Chains 459 Ch. 9 Introduction to Queueing Theory 499 App. A. Mathematical Tables 571 App. B. Tables of Fourier Transforms 574 App. C. Computer Programs for Generating Random Variables 576 Answers to Selected Problems 580 Index 589