概率论基础教程 (英文版) (第8版) pdf epub mobi txt 电子书 下载 2025

想要找书就要到 小美书屋

立刻按 ctrl+D收藏本页

你会得到大惊喜!!

作者：（美国）罗斯（Sheldon M.Ross）

罗斯（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Epstei”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年至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 Enginearing 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。

1 COMBINATORIAL ANALYSIS
1.1 Introduction
1.2 The Basic Prinaple 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 VARIABLFS
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
4.7 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
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 oflndependent Random Variables
6.4 Conditional Distributions： Discrete
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
7.5 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 Techrliques Answers to Seiected Problems
Solutions to Self—Test Problems and Exercises
Index
· · · · · · (收起)