Textbook

Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 2nd Edition

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This text introduces engineering students to probability theory and stochastic processes. Along with a thorough mathematical development of the subject, the book presents intuitive explanations of key points in order to give students the insights they need to apply the math to practical engineering problems. For each new principle it presents, the book provides an example of the application of the mathematics to an engineering problem. Each section ends with a quiz (with solutions posted on the book's web site) to help students gauge their understanding of the new material. Homework problems are annotated with symbols indicating their degree of difficulty. The initial five chapters contain the core material that will be present in any introductory course. In one-semester undergraduate courses, instructors will select material from the remaining seven chapters to meet their individual goals. Graduate students can cover all twelve chapters in one semester. All the techniques in the book are derived from a single, unifying model of an experiment consisting of a procedure and observations. The mathematical exposition begins with the axioms of probability and proceeds with clearly annotated definitions and theorems that convey the logical structure of the theory. To help students quickly understand the underlying principles of probability theory and to learn how to solve practical problems, the book introduces discrete random variables and continuous random variables in separate chapters.

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Table of Contents

Features of this Text.

Preface.

1. Experiments, Models, and Probabilities.

2. Discrete Random Variables.

3. Continuous Random Variables.

4. Pairs of Random Variables.

5. Random Vectors.

6. Sums of Random Variables.

7. Parameter Estimation Using the Sample Mean.

8. Hypothesis Testing.

9. Estimation of a Random Variable.

10. Stochastic Processes.

11. Random Signal Processing.

12. Markov Chains.

Appendix A: Families of Random Variables.

Appendix B: A Few Math Facts.

References.

Index.

Author Information

Dr. Roy Yates received the B.S.E. degree in 1983 from Princeton University, and the S.M. and Ph.D. degrees in 1986 and 1990 from M.I.T., all in Electrical Engineering. Since 1990, he has been with the Wireless Information Networks Laboratory (WINLAB) and the ECE department at Rutgers, University. He is currently an associate professor.

David J. Goodman is Director of WINLAB and a Professor of Electrical and Computer Engineering at Rutgers University. Before coming to Rutgers, he enjoyed a twenty year research career at Bell Labs where he was a Department Head in Communications Systems Research. He has made fundamental contributions to digital signal processing, speech coding, and wireless information networks.

New To This Edition

A tutorial in each chapter on using MATLAB to understand and apply the theory introduced in the chapter.
Material reorganized and expanded based on authors' teaching experience and feedback from students and instructors.
Expanded coverage of applications of probability including estimation, hypothesis testing, and signal processing.
A new chapter on random vectors.
Coverage of Markov chains and elementary queuing theory made more accessible, with fewer preliminary details to absorb before reaching the subjects of greatest practical importance.

Hallmark Features

Student-friendly narrative aids intuitive understanding of real-world priciples underlying mathematical concepts. Motivates engineering students who often perceive the subject as abstract and irrelevant to their practical interests.
MATLAB applications encourage students to gain hands-on experience with material presented in the text. Instructors have access to program files provided by the authors, and do not need to create and debug their own code.
Extensive collection of exercises (examples, quizzes, and homework problems). Degree of-difficulty of each homework problem clearly labeled. Assists students with the most difficult part of learning probability—going from theory to practical applications.
Separate treatment of discrete and continuous random variables. Students, especially those encountering material for the first time, can learn basic principles without the confusion of two different ways of calculating probabilities and averages (sums and integrals). Seeing the same principles twice reinforces the learning experience.
WWW support for instructors, especially including solutions to homework problems. Graphical interface enables instructors to generate customized solutions handouts and offers flexibility in the way homework problems are used. Saves instructor time in solving exercises and preparing information for students.

Professor Reviews

"Excellent book. One of the best as a teaching tool. Yates' book is far better than Papoulis' book."

Aldo Morales, Penn State Harrisburg

Related Websites

Instructor Companion Site

Student Companion Site