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, Markov's and Chevyshev's 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.

Michael Mitzenmacher 1996年于加州大学伯克利分校获得博士学位，现为哈佛大学计算机科学教授。在1999年进入哈佛大学之前，他是Palo Alto数字系统研究实验室的研究人员。他曾获美国科学基金（NSF）CAAREER奖和Alfred P. Sloan研究基金。2002年，由于在纠错码方面的出色工作，他获得了IEEE信息论学会的“最佳论文”奖。