本书叙述深入浅出，涉及面广。主要内容有变量、条件概率及条件期望、离散及连续马尔可夫链、指数分布、泊松过程、布朗运动及平稳过程、更新理论及排队论等；也包括了过程在物理、生物、运筹、网络、遗传、经济、保险、金融及性中的应用。特别是有关模拟的内容，给系统运行的模拟计算提供了有力的工具。除正文外，本书有约700道习题，其中带星号的习题还提供了解答。
本书可作为概率论与统计、计算机科学、保险学、物理学、社会科学、生命科学、管理科学与工程学等专业过程基础课教材。
Sheldon M.Ross国际知名概率与统计学家，南加州大学工业工程与运筹系系主任。毕业于斯坦福大学统计系，曾在加州大学伯克利分校任教多年。研究领域包括：模型.仿真模拟、统计分析、金融数学等：Ross教授著述颇丰，他的多种畅销数学和统计教材均产生了世界性的影响，如Int
1.Introduction to Probability Theory
1.1.Introduction
1.2.Sample Space and Events
1.3.Probabilities Defined on Events
1.4.Conditional Probabilities
1.5.Independent Events
1.6.Bayes' Formula
Exercises
References
2.Random Variables
2.1.Random Variables
2.2.Discrete Random Variables
2.3.Continuous Random Variables
2.4.Expectation of a Random Variable
2.5.Jointly Distributed Random Variables
2.6.Moment Generating Functions
2.7.Limit Theorems
2.8.Stochastic Processes
Exercises
References
3.Conditional Probability and Conditional Expectation
3.1.Introduction
3.2.The Discrete Case
3.3.The Continuous Case
3.4.Computing Expectations by Conditioning
3.5.Computing Probabilities by Conditioning
3.6.Some Applications
3.7.An Identity for Compound Random Variables
Exercises
4.Markov Chains
4.1.Introduction
4.2.Chapman-Kolmogorov Equations
4.3.Classification of States
4.4.Limiting Probabilities
4.5.Some Applications
4.6.Mean Time Spent in Transient States
4.7.Branching Processes
4.8.Time Reversible Markov Chains
4.9.Markov Chain Monte Carlo Methods
4.10.Markov Decision Processes
4.11.Hidden Markov Chains
Exercises
References
5.The Exponential Distribution and the Poisson Process
5.1.Introduction
5.2.The Exponential Distribution
5.3.The Poisson Process
5.4.Generalizations of the Poisson Process
Exercises
References
6.Continuous-Time Markov Chains
7.Renewal Theory and Its Applications
8.Queueing Theory
9.Reliability Theory
10.Brownian Motion and Stationary Processes
11.Simulation
Appendix: Solutions to Starred Exercises
Index
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