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This text introduces engineering students to probability theory and stochastic processes. Along with 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 math to practical engineering problems. The first seven chapters contain the core material that is essential to any introductory course. In one-semester undergraduate courses, instructors can select material from the remaining chapters to meet their individual goals. Graduate courses can cover all chapters in one semester.
Probability and Stochastic Processes A Friendly Introduction for Electrical and Computer Engineers
by Yates, Roy D.; Goodman, David J.Edition: 3rd
Format: Paperback
Pub. Date: 2014-01-28
Publisher(s): Wiley
List Price: $220.57
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Summary
Author Biography
Roy Yates received the B.S.E. degree in 1983 from Princeton and the S.M. and Ph.D. degrees in 1986 and 1990 from MIT, all in Electrical Engineering. Since 1990, he has been with the Wireless Information Networks Laboratory (WINLAB) and the ECE department at Rutgers University. Presently, he is an Associate Director of WINLAB and a Professor in the ECE Dept. He is a co-author (with David Goodman) of the text Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers published by John Wiley and Sons. He is a co-recipient (with Christopher Rose and Sennur Ulukus) of the 2003 IEEE Marconi Prize Paper Award in Wireless Communications. His research interests include power control, interference suppression and spectrum regulation for wireless systems.
Table of Contents
Chapter 1. Experiments, Models, and Probabilities
Chapter 2. Sequential Experiments
Chapter 3. Discrete Random Variables
Chapter 4. Continuous Random Variables
Chapter 5. Multiple Random Vectors
Chapter 6. Probability Models of Derived Random Variables
Chapter 7. Conditional Probability Models
Chapter 8. Random Vectors
Chapter 9. Sums of Random Variables
Chapter 10. The Sample Mean
Chapter 11. Hypothesis Testing
Chapter 12. Estimation of a Random Variable
Chapter 13. Stochastic Processes
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