Basic principles and applications of probability theory概率论基础原理与应用
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作者： A.V. Skorokhod著
出 版 社： 新世纪出版社
出版时间： 2004-11-1字数：版次：页数： 282印刷时间： 2004/11/01开本： 16开印次：纸张： 胶版纸I S B N ： 9783540546863包装： 精装内容简介
The book is an introduction to modern probability theory written by one of the famous experts in this area. Readers will learn about the basic concepts of probability and its applications, preparing them for more advanced and specialized works.
目录
1Introduction
1.1 The Nature of Randomness
1.1.1 Determinism and Chaos
1.1.2 Unpredictability and Randomness
1.1.3 Sources of Randomness
1.1.4 The Role of Chance
1.2 Formalization of Randomness
1.2.1 Selection from Among Several Possibilities. Experiments. Events
1.2.2 Relative Frequencies. Probability as an Ideal Relative Frequency
1.2.3 The Definition of Probability
1.3 Problems of Probability Theory
1.3.1 Probability and Measure Theory
1.3.2 Independence
1.3.3 Asymptotic Behavior of Stochastic Systems
1.3.4 Stochastic Analysis
2Probability Spacer
2.1 Finite Probability Space
2.1.1 Combinatorial Analysis
2.1.2 Conditional Probability
2.1.3 Bernoulli's Scheme. Limit Theorems
2.2 Definition of Probability Space
2.2.1 a-algebras. Probability
2.2.2 Random Variables. Expectation
2.2.3 Conditional Expectation
2.2.4 Regular Conditional Distributions
2.2.5 Spaces of Random Variables. Convergence
2.3 Random Mappings
2.3.1 Random Elements
2.3.2 Random Functions
2.3.3 Random Elements in Linear Spaces
2.4 Construction of Probability Spaces
2.4.1 Finite-dimensional Space
2.4.2 Function Spaces
2.4.3 Linear Topological Spaces. Weak Distributions
2.4.4 The Minlos-Sazonov Theorem
3 Independence
3.1 Independence of a-Algebras
3.1.1 Independent Algebras
3.1.2 Conditions for the Independence of a-Algebras
3.1.3 Infinite Sequences of Independent a-Algebras
3.1.4 Independent Random Variables
3.2 Sequences of Independent Random Variables
3.2.1 Sums of Independent Random Variables
3.2.2 Kolmogorov's Inequality
3.2.3 Convergence of Series of Independent Random Variables
3.2.4 The Strong Law of Large Numbers
3.3 Random Walks
3.3.1 The Renewal Scheme
3.3.2 Recurrency
3.3.3 Ladder Funetionals
3.4 Processes with Independent Increments
3.4.1 Definition
3.4.2 Stochastically Continuous Processes
3.4.3 L6vy's Formula
3.5 Product Measures
3.5.1 Definition
3.5.2 Absolute Continuity and Singularity of Measures
3.5.3 Kakutani's Theorem
3.5.4 Absolute Continuity of Gaussian Product Measures
4General Theory of Stochastic Processes and Random Functions
4.1 Regular Modifications
4.1.1 Separable Random Functions
4.1.2 Continuous Stochastic Processes
4.1.3 Processes With at Most Jump Discontinuities.
4.1.4 Markov Processes
4.2 Measurability
4.2.1 Existence of a Measurable Modification
4.2.2 Mean-Square Integration
4.2.3 Expansion of a Random Function in an Orthogonal Series
……
5 Limit Theorems
Historic and Bibliographic Comments
References
