本書敘述深入淺出，涉及面廣。主要內容有變量、條件概率及條件期望、離散及連續馬爾可夫鏈、指數分布、泊松過程、布朗運動及平穩過程、更新理論及排隊論等；也包括了過程在物理、生物、運籌、網絡、遺傳、經濟、保險、金融及性中的應用。特別是有關模擬的內容，給系統運行的模擬計算提供了有力的工具。除正文外，本書有約700道習題，其中帶星號的習題還提供了解答。
本書可作為概率論與統計、計算機科學、保險學、物理學、社會科學、生命科學、管理科學與工程學等專業過程基礎課教材。
Sheldon M.Ross國際知名概率與統計學家，南加州大學工業工程與運籌系系主任。畢業于斯坦福大學統計系，曾在加州大學伯克利分校任教多年。研究領域包括：模型.仿真模擬、統計分析、金融數學等：Ross教授著述頗豐，他的多種暢銷數學和統計教材均產生了世界性的影響，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
![LPI LINUX認證指南[影印版]](/upload/thumbnail/book/201711/28/340eb-173c9.jpg_300x405.jpg)
