Probability in Electrical Engineering and Computer Science: An Application-Driven Course

The book is designed for a junior/senior level course. Applications drive the material: PageRank, Multiplexing, Digital Link, Tracking, Speech Recognition, Route Planning and more. Topics include Markov chains, detection, coding, estimation, Viterbi algorithm, expectation maximization, clustering, compressed sensing, recommender systems, Kalman Filter, Markov decision problems, LQG, and channel capacity. Matlab examples are used to simulate models and to implement the algorithms. Appendices provide the necessary background in basic probability and linear algebra. See https://sites.google.com/site/walrandpeecs/home.

Jean Walrand received his Ph.D. in EECS from UC Berkeley and has been on the faculty of that department since 1982. He is the author of An Introduction to Queueing Networks (Prentice Hall, 1988) and of Communication Networks: A First Course (2nd ed. McGraw-Hill,1998) and co-author of High-Performance Communication Networks (2nd ed, Morgan Kaufman, 2000), of Communication Networks: A Concise Introduction (Morgan & Claypool, 2010), and of Scheduling and Congestion Control for Communication and Processing networks (Morgan & Claypool, 2010). His research interests include stochastic processes, queuing theory, communication networks, game theory and the economics of the Internet. Prof. Walrand is a Fellow of the Belgian American Education Foundation and of the IEEE and a recipient of the Lanchester Prize, the Stephen O. Rice Prize, the IEEE Kobayashi Award and the ACM Sigmetrics Achievement Award.