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內容簡介

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）.

內頁插圖

目錄

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

概率論和隨機過程（第2版） [Theory of Probability and Random Processes] 下載 mobi epub pdf txt 電子書

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講解不錯，看英文原文順便練習英文能力瞭

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這次滿減買的 買瞭N多quant書存起來慢慢看

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好像很好的書很不錯的建議推薦

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不錯，內容豐富，包裝精美，推薦大傢購買。

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這本書覆蓋瞭從入門機械製圖工程師/技師所必需知道的關於産業的知識。書中還覆蓋瞭所必需的進階知識。 《實分析教程(第2版)(英文影印版)》是一部備受專傢好評的教科書，書中用現代的方式清晰論述瞭實分析的概念與理論，定理證明簡明易懂，可讀性強。在第一版的基礎上做瞭全麵修訂，有200道例題，練習題由原來的1200道增加到1300習題。本書的寫法像一部文學讀物，這在數學教科書很少見，因此閱讀本書會是一種享受。

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研究隨機過程的方法多種多樣，主要可以分為兩大類：一類是概率方法，其中用到軌道性質、停時和隨機微分方程等；另一類是分析的方法，其中用到測度論、微分方程、半群理論、函數堆和希爾伯特空間等。實際研究中常常兩種方法並用。另外組閤方法和代數方法在某些特殊隨機過程的研究中也有一定作用。研究的主要內容有：多指標隨機過程、無窮質點與馬爾可夫過程、概率

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　　 目次：全書其有四部分，新增加瞭5章，總共17章。（一）集閤論、實數和微積分：集閤論；實數體係和微積分。（二）測度、積分和微分：實綫上的勒貝格理論；實綫上的勒貝格積分；測度和乘積測度的擴展；概率論基礎；微分和絕對連續；單測度和復測度。（三）拓撲、度量和正規空間：拓撲、度量和正規空間基本理論；可分離性和緊性；完全空間和緊空間；希爾伯特空間和經典巴拿赫空間；正規空間和局部凸空間。（四）調和分析、動力係統和hausdorff側都：調和分析基礎；可測動力係統；hausdorff測度和分形。

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