Probability and Computing: Randomized Algorithms and Probabilistic Analysis

Assuming only an elementary background in discrete mathematics this textbook is an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It includes random sampling expectations Markovs and Chevyshevs inequalities Chernoff bounds balls and bins models the probabilistic method Markov chains MCMC martingales entropy and other topics. The book is designed to accompany a one- or two-semester course for graduate students in computer science and applied mathematics.