概率论和随机过程（第2版） [Theory of Probability and Random Processes] 下载 mobi epub pdf 电子书 2024

☆☆☆☆☆
简体网页||繁体网页



点击这里下载

类似图书 点击查看全场最低价

---

内容简介

This book is primarily based on a one-year course that has been taught for a number of years at Princeton University to advanced undergraduate and graduate students. During the last year a similar course has also been taught at the University of Maryland.
We would like to express our thanks to Ms. Sophie Lucas and Prof. Rafael Herrera who read the manuscript and suggested many corrections. We are particularly grateful to Prof. Boris Gurevich for making many important sug-gestions on both the mathematical content and style.
While writing this book， L. Koralov was supported by a National Sci-ence Foundation grant （DMS-0405152）. Y. Sinai was supported by a National Science Foundation grant （DMS-0600996）.

内页插图

目录

Part Ⅰ Probability Theory
1 Random Variables and Their Distributions
1.1 Spaces of Elementary Outcomes， a-Algebras， and Measures
1.2 Expectation and Variance of Random Variables on a Discrete Probability Space
1.3 Probability of a Union of Events
1.4 Equivalent Formulations of a-Additivity， Borel a-Algebras and Measurability
1.5 Distribution Functions and Densities
1.6 Problems
2 Sequences of Independent Trials
2.1 Law of Large Numbers and Applications
2.2 de Moivre-Laplace Limit Theorem and Applications
2.3 Poisson Limit Theorem.
2.4 Problems
3 Lebesgue Integral and Mathematical Expectation
3.1 Definition of the Lebesgue Integral
3.2 Induced Measures and Distribution Functions
3.3 Types of Measures and Distribution Functions
3.4 Remarks on the Construction of the Lebesgue Measure
3.5 Convergence of Functions， Their Integrals， and the Fubini Theorem
3.6 Signed Measures and the R，adon-Nikodym Theorem
3.7 Lp Spaces
3.8 Monte Carlo Method
3.9 Problems
4 Conditional Probabilities and Independence
4.1 Conditional Probabilities
4.2 Independence of Events， Algebras， and Random Variables
4.3
4.4 Problems
5 Markov Chains with a Finite Number of States
5.1 Stochastic Matrices
5.2 Markov Chains
5.3 Ergodic and Non-Ergodic Markov Chains
5.4 Law of Large Numbers and the Entropy of a Markov Chain
5.5 Products of Positive Matrices
5.6 General Markov Chains and the Doeblin Condition
5.7 Problems
6 Random Walks on the Lattice Zd
6.1 Recurrent and Transient R，andom Walks
6.2 Random Walk on Z and the Refiection Principle
6.3 Arcsine Law
6.4 Gambler's Ruin Problem
6.5 Problems
7 Laws of Larze Numbers
7.1 Definitions， the Borel-Cantelli Lemmas， and the Kolmogorov Inequality
7.2 Kolmogorov Theorems on the Strong Law of Large Numbers
7.3 Problems
8 Weak Converaence of Measures
8.1 Defnition of Weak Convergence
8.2 Weak Convergence and Distribution Functions
8.3 Weak Compactness， Tightness， and the Prokhorov Theorem
8.4 Problems
9 Characteristic Functions
9.1 Definition and Basic Properties
9.2 Characteristic Functions and Weak Convergence
9.3 Gaussian Random Vectors
9.4 Problems
10 Limit Theorems
10.1 Central Limit Theorem， the Lindeberg Condition
10.2 Local Limit Theorem
10.3 Central Limit Theorem and Renormalization GrOUD Theorv
10.4 Probabilities of Large Deviations
……
Part Ⅱ Random Processes
Index

前言/序言

概率论和随机过程（第2版） [Theory of Probability and Random Processes] 下载 mobi epub pdf txt 电子书 格式

评分☆☆☆☆☆

本书为英文原版内容不错，对学习数学有很大帮助，是本不错的工具书，同时有助于提高英语水平，但是发货的包装对书的保护严重不足。

评分☆☆☆☆☆

与位势及各种特殊过程的专题讨论等。中

评分☆☆☆☆☆

好像很好的书很不错的建议推荐

评分☆☆☆☆☆

好好好啊啊哦啊好好爱好好好阿红啊

评分☆☆☆☆☆

　　 目次：全书其有四部分，新增加了5章，总共17章。（一）集合论、实数和微积分：集合论；实数体系和微积分。（二）测度、积分和微分：实线上的勒贝格理论；实线上的勒贝格积分；测度和乘积测度的扩展；概率论基础；微分和绝对连续；单测度和复测度。（三）拓扑、度量和正规空间：拓扑、度量和正规空间基本理论；可分离性和紧性；完全空间和紧空间；希尔伯特空间和经典巴拿赫空间；正规空间和局部凸空间。（四）调和分析、动力系统和hausdorff侧都：调和分析基础；可测动力系统；hausdorff测度和分形。

评分☆☆☆☆☆

的定义为一组随机变量，即指定一参数集，对于其中每一参数点t指定一个随机变量x(t)。如果回忆起随机变量自身就是一个函数，以ω表示随机变量x(t)的定义域中的一点，并以x(t，ω)表示随机变量在ω的值，则随机过程就由刚才定义的点偶(t，ω)的函数以及概率的分配完全确定。如果固定t，这个二元函数就定义一个ω的函数，即以x(t)表示的随机变量。如果固定ω，这个二元函数就定义一个t的函数，这是过程的样本函数。概率

评分☆☆☆☆☆

与位势及各种特殊过程的专题讨论等。中

评分☆☆☆☆☆

首页有个黄色斑。书很专业，是我期待的。

评分☆☆☆☆☆

类似图书 点击查看全场最低价