A nonmeasure theoretic introduction to stochastic processes. Considers its diverse range of applications and provides readers with probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments; a new chapter on Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs. Numerous exercises and problems have been added throughout the text.

For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.

《概率论教程:英文版(第3版)》是一本享誉世界的经典概率论教材，令众多读者受益无穷。自出版以来。已被世界75％以上的大学的数万名学生使用。《概率论教程:英文版(第3版)》内容丰富，逻辑清晰，叙述严谨。不仅可以拓展读者的视野。而且还将为其后续的学习和研究打下坚实基础。此外，《概率论教程:英文版(第3版)》的习题较多，都经过细心的遴选，从易到难，便于读者巩固练习。本版补充了有关测度和积分方面的内容，并增加了一些习题。

《随机过程》是日本著名数学家伊藤清的著作，是随机过程方面的经典名著，篇幅短小，叙述精辟，具有较高的理论水平。书中以简练的笔法介绍了随机过程论的主要方面，包括可加过程、平稳过程和Markoff过程，并概述了一维扩散过程。具有初步概率论和泛函分析知识的读者，可以借此快速掌握随机过程的基本理论。