☆☆☆☆☆




點擊這裡下載

類似圖書 點擊查看全場最低價

---

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

評分☆☆☆☆☆

評分☆☆☆☆☆

隨機過程

評分☆☆☆☆☆

學習

評分☆☆☆☆☆

　　 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。

評分☆☆☆☆☆

這次滿減買的 買瞭N多quant書存起來慢慢看

評分☆☆☆☆☆

Sinal是統計物理學的數學問題的國際權威，特彆是對遍曆理論有突齣貢獻．他首次給齣任意保測映射的不變量摘以嚴格定義．其後證明彈子運動的遍曆性以及擬周期Schr?dinger方程的譜性質.

評分☆☆☆☆☆

英文影印版，經典之作啊，要好好學習一下瞭

評分☆☆☆☆☆

剛收到書，還沒有看，希望能讀完吧，大傢的評價都還不錯。

評分☆☆☆☆☆

這個概率論我本科的時候沒學好，現在補一補

類似圖書 點擊查看全場最低價

查看全網最低價