　　概率论是研究自然界和人类社会中随机现象数量规律的数学分支，《华章数学原版精品系列：概率论基础教程（英文版·第8版）》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。《华章数学原版精品系列：概率论基础教程（英文版·第8版）》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。《华章数学原版精品系列：概率论基础教程（英文版·第8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，也可供应用工作者参考。

作者简介

　　罗斯（Sheldon Ross），世界著名的应用概率专家和统计学家，现为南加州大学工业与系统工程系Epstein讲座教授。他于1968年在斯坦福大学获得统计学博士学位，在1976年～2004年期间于加州大学伯克利分校任教，其研究领域包括统计模拟、金融工程、应用概率模型、随机动态规划等。Ross教授创办了《Probability in the Engirleering and Informational Sciences》杂志并一直担任主编，他的多种畅销教材均产生了世界性的影响，其中《统计模拟（第5版）》和《随机过程（第2版）》等均由机械工业出版社引进出版。

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精彩书评

　　★“这是一本非常优秀的概率论入门教材，是我所见过的最好的一本。”
　　——Nhu Nguyen（新墨西哥州立大学）
　　
　　★“本书示例丰富、实用，写作风格清新、流畅，解答详细、准确，是一本通俗易懂的教材……”
　　——Robert Bauer（伊利诺伊大学厄巴纳-尚佩恩分校）

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

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

3 CONDITIONAL PROBABILITY AND INDEPENDENCE
3.1 Introduction
3.2 Conditional Probabilities
3.3 Bayes's Formula
3.4 Independent Events
3.5 P(F) Is a Probability

4 RANDOM VARIABLES
4.1 Random Variables it
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
4.7 The Poisson Random Variable
4.8 Other Discrete Probability Distributions
4.9 Expected Value of Sums of Random Variables
4.10 Properties of the Cumulative Distribution Function

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
5.5 Exponential Random Variables
5.6 Other Continuous Distributions
5.7 The Distribution of a Function of a Random Variable

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

7 PROPERTIES OF EXPECTATION
7.1 Introduction
7.2 Expectation of Sums of Random Variables
7.3 Moments of the Number of Events that Occur
7.4 Covariance, Variance of Sums, and Correlations
7.S Conditional Expectation
7.6 Conditional Expectation and Prediction
7.7 Moment Generating Functions
7.8 Additional Properties of Normal Random Variables
7.9 General Definition of Expectation

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 When Approximating a Sum of Independent Bernoulli Random Variables by a Poisson Random Variable

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

10 SIMULATION
10.1 Introduction
10.2 General Techniques for Simulating Continuous Random Variables
10.3 Simulating from Discrete Distributions
10.4 Variance Reduction Techniques

Answers to Selected Problems

Solutions to Self-Test Problems and Exercises

Index