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这个教材是麻省理工的概率课使用的教材，很经典。有需要的来下载。
内容简介《概率论基础教程》(英文版•第5版)是全球高校广泛采用的概率论教材，通过大量的例子讲述了概率论的基础知识，主要内容有组合分析概率论公理化、条件概率和独立性、离散和连续型随机变量、随机变量的联合分布、期望的性质、极限定理等．本书附有大量的练习，分为习题、理论习题和自检习题三大类，其中自检习题部分还给出全部解答。
作者简介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：随机模型、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Simulation（《统计模拟》）、Introduction to Probability Models（《应用随机过程：概率模型导论》）等（均由人民邮电出版社出版）。
编辑推荐《概率论基础教程》(英文版•第7版)作为概率论的入门书，适用于大专院校数学、统计、工程和相关专业(包括计算科学、生物、社会科学和管理科学)的学生阅读，也可供概率应用工作者参考。
目录
1 Combinatorial Analysis
1.1 Introduction
1.2 The Basic Principle of Counting
1.3 Permutations
1.4 Combinations
1.5 Multinomial Coefficients
1.6 The Number of Integer Solutions of Equations*
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
2 Axioms of Probability
2.1 Introduction
2.2 Sample Space and Events
2.3 Axioms of Probability
2.4 Some Simple Propositions
2.5 Sample Spaces Having Equally Likely Outcomes
2.6 Probability as a Continuous Set Function*
2.7 Probability as a Measure of Belief
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
3 Conditional Probability and Independence
3.1 Introduction
3.2 Conditional Probabilities
3.3 Bayes' Formula
3.4 Independent Events
3.5 P(.|F) Is a Probability
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
4 Random Variables
4.1 Random Variables
4.2 Discrete Random Variables
4.3 Expected Value
4.4 Expectation of a Function of a Random Variable
4.5 Variance
4.6 The Bernoulli and Binomial Random Variables
4.6.1 Properties of Binomial Random Variables
4.6.2 Computing the Binomial Distribution Function
4.7 The Poisson Random Variable
4.7.1 Computing the Poisson Distribution Function
4.8 Other Discrete Probability Distributions
4.8.1 The Geometric Random Variable
4.8.2 The Negative Binomial Random Variable
4.8.3 The Hypergeometric Random Variable
4.8.4 The Zeta (or Zipf) Distribution
4.9 Properties of the Cumulative Distribution Function
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
5 Continuous Random Variables
5.1 Introduction
5.2 Expectation and Variance of Continuous Random Variables
5.3 The Uniform Random Variable
5.4 Normal Random Variables
5.4.1 The Normal Approximation to the Binomial Distribution
5.5 Exponential Random Variables
5.5.1 Hazard Rate Functions
5.6 Other Continuous Distributions
5.6.1 The Gamma Distribution
5.6.2 The Weibull Distribution
5.6.3 The Cauchy Distribution
5.6.4 The Beta Distribution
5.7 The Distribution of a Function of a Random Variable
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
6 Jointly Distributed Random Variables
6.1 Joint Distribution Functions
6.2 Independent Random Variables
6.3 Sums of Independent Random Variables
6.4 Conditional Distributions: Discrete Case
6.5 Conditional Distributions: Continuous Case
6.6 Order Statistics*
6.7 Joint Probability Distribution of Functions of Random Variables
6.8 Exchangeable Random Variables*
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
7 Properties of Expectation
7.1 Introduction
7.2 Expectation of Sums of Random Variables
7.2.1 Obtaining Bounds from Expectations via the Probabilistic Method*
7.2.2 The Maximum-Minimums Identity*
7.3 Moments of the Number of Events that Occur
7.4 Covariance, Variance of Sums, and Correlations
7.5 Conditional Expectation
7.5.1 Definitions
7.5.2 Computing Expectations by Conditioning
7.5.3 Computing Probabilities by Conditioning
7.5.4 Conditional Variance
7.6 Conditional Expectation and Prediction
7.7 Moment Generating Functions
7.7.1 Joint Moment Generating Functions
7.8 Additional Properties of Normal Random Variables
7.8.1 The Multivariate Normal Distribution
7.8.2 The Joint Distribution of the Sample Mean and Sample Variance
7.9 General Definition of Expectation
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
8 Limit Theorems
8.1 Introduction
8.2 Chebyshev's Inequality and the Weak Law of Large Numbers
8.3 The Central Limit Theorem
8.4 The Strong Law of Large Numbers
8.5 Other Inequalities
8.6 Bounding The Error Probability
Summary
Problems
Theoretical Exercises
Self-Test Problems and Exercises
9 Additional Topics in Probability
9.1 The Poisson Process
9.2 Markov Chains
9.3 Surprise, Uncertainty, and Entropy
9.4 Coding Theory and Entropy
Summary
Theoretical Exercises
Self-Test Problems and Exercises
10 Simulation
10.1 Introduction
10.2 General Techniques for Simulating Continuous Random Variables
10.2.1 The Inverse Transformation Method
10.2.2 The Rejection Method
10.3 Simulating from Discrete Distributions
10.4 Variance Reduction Techniques
10.4.1 Use of Antithetic Variables
10.4.2 Variance Reduction by Conditioning
10.4.3 Control Variates
Summary
Problems
Self-Test Problems and Exercises
APPENDICES
A Answers to Selected Problems
B Solutions to Self-Test Problems and Exercises
Index
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