概率論基礎教程 (英文版) (第8版) pdf epub mobi txt 電子書 下載2025

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作者：（美國）羅斯（Sheldon M.Ross）

羅斯（Sheldon M.Ross）世界著名的應用概率專傢和統計學傢，現為南加州大學工業與係統工程係Epstei”講座教授。他於1968年在斯坦福大學獲得統計學博士學位，在1976年至2004年期間於加州大學伯剋利分校任教—其研究領域包括統計模擬、金融工程、應用概率模型、隨機動態規劃等。Ross教授創辦瞭《Probability in the Enginearing and Informational Sciences》雜誌並一直擔任主編，他的多種暢銷教材均産生瞭世界性的影響，其中《統計模擬（第5版）》和《隨機過程（第2版）》等均由機械工業齣版社引進齣版。

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
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