Sheldon M．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。
目錄：
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
AXIOMS OF PROBABILITY
Introduction
Sample Space and Events
Axioms of Probability
Some Simple Propositions
Sample Spaces Having Equally Likely
Outcomes
Probability as a Continuous Set Function
Probability as a Measure of Belief
CONDITIONAL PROBABILITY
AND INDEPENDENCE
3.1 Introduction
3.2 Conditional Probabilities
3.3 Bayes''s Formula
3.4 Independent Events
3.S PF 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
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
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
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
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
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
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
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