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

by Roy D. Yates (Rutgers University, NJ); David Goodman (Polytechnic University, NY)

• ISBN13: 9780471272144
• ISBN10: 0471272140
• Edition: 2nd
• Format: Hardcover
• Copyright: 2004-05-01
• Publisher: Wiley
• More Book Details
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Summary

This user-friendly resource will help you grasp the concepts of probability and stochastic processes, so you can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems. Key Features: The text follows a single model that begins with an experiment consisting of a procedure and observations. The mathematics of discrete random variables appears separately from the mathematics of continuous random variables. Stochastic processes are introduced in Chapter 6, immediately after the presentation of discrete and continuous random variables. Subsequent material, including central limit theorem approximations, laws of large numbers, and statistical inference, then use examples that reinforce stochastic process concepts. An abundance of exercises are provided that help students learn how to put the theory to use.

Author Biography

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.

Table of Contents

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

Appendices

References

Index