機率論基礎[清華大學出版社2010年版外文圖書]

圖書信息

出版社:清華大學出版社; 第1版 (2010年12月1日)

作者：雅科蒂(Jean Jacod)、普羅特(Philip Protter)

外文書名:Probability Essentials

叢書名:數學圖書影印版系列

平裝:254頁

語種：英語

開本:16

ISBN:7302241554, 9787302241553

條形碼:9787302241553

商品尺寸:22.6 x 18.4 x 1.2 cm

商品重量:340 g

機率論基礎[清華大學出版社2010年版外文圖書]

品牌:清華大學出版社

圖書簡介

This introduction to Probability Theory can be used，at the beginning graduate level．for a one—semester course on Probability Theory or for self-direction without benefit of a formal course：the measure theory needed iS developed in the text．It will also be useful for students and teachers in related areaS such as Finance Theory （Economics），Electrical Engineerin9，and Operations Research．The text covers the essentials in a directed and lean way with 28 short chapters．Assuming of readers only an undergraduate background in mathematics，it brings them from a starting knowledge ofthe subject to a knowledge ofthe basics ofMartingale Theory．Afler learning Probability Theory foFin this text，the interested student will be ready to continue with the study of more advanced topics，such as Brownian Motion andIto Calculus．or Statistical Inference．The second edition contains some additionsto the text and to the references and some parts are completely

目錄

1 Introduction

2 Axioms of Probability

3 Conditional Probability and Independence

4 Probabilities on a Finite or Countable Space

5 Random Variables on a Countable Space

6 Construction of a Probability Measure

7 Construction of a Probability Measure on R

8 Random Variables

9 Integration with Respect to a Probability Measure

10 Independent Random Variables

11 Probability Distributions on R

12 Probability Distributions on R”

13 Characteristic Functions

14 Properties of Characteristic Functions

15 Sums of Independent Random Variables

16 Gaussian Random Variables（The Normal and the Multivariate Normal Distributions）

17 Convergence of Random Variables

18 Weak Convergence

19 Weak Convergence and Characteristic Functions

20 The Laws of Large Numbers

……

References

Index