具體描述
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.
用戶評價
##如果有人想知道學一點初等概率論之後可以乾什麼，推薦讀這本書
評分##配閤 randomized algorithms 來看，裏麵有些相同的內容
評分 評分##完全因為封麵好看去讀的
評分概率部分基本上是從頭講起的 沒學過概率也能看懂 感動 算法分析講的好
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