具体描述
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.
用户评价
##很难，全部是数学理论，推导。我觉得这本数应该算数学书多一些。
评分##完全因为封面好看去读的
评分概率部分基本上是从头讲起的 没学过概率也能看懂 感动 算法分析讲的好
评分##从计算机科学的角度理解concentration, probabilistic method, Markov chain, entropy和martingale，用离散的眼光对待概率和计算之间的关系，真是妙不可言
评分##从计算机科学的角度理解concentration, probabilistic method, Markov chain, entropy和martingale，用离散的眼光对待概率和计算之间的关系，真是妙不可言
评分##如果有人想知道学一点初等概率论之后可以干什么，推荐读这本书
评分##之前因为封面好看tag了这个 = = 然后我现在真的在学这门课…… 什么，你说你结课了就妄想自己真的读完这本书了？（
评分##配合 randomized algorithms 来看，里面有些相同的内容
评分##如果有人想知道学一点初等概率论之后可以干什么，推荐读这本书
