《機率論教程:英文版(第3版)》是一本享譽世界的經典機率論教材，令眾多讀者受益無窮。自出版以來。已被世界75%以上的大學的數萬名學生使用。《機率論教程:英文版(第3版)》內容豐富，邏輯清晰，敘述嚴謹。不僅可以拓展讀者的視野。而且還將為其後續的學習和研究打下堅實基礎。此外，《機率論教程:英文版(第3版)》的習題較多，都經過細心的遴選，從易到難，便於讀者鞏固練習。本版補充了有關測度和積分方面的內容，並增加了一些習題。
基本介紹
- 書名：機率論教程:英文版
- 作者：鐘開萊(Kai Lai Chung)
- 出版社：機械工業出版社
- 頁數：419頁
- 開本：32
- 品牌：機械工業出版社
- 外文名：A Course In Probability Theory
- 類型：科學與自然
- 出版日期：2010年4月1日
- 語種：英語
- ISBN：9787111302896, 7111302893
基本介紹,內容簡介,作者簡介,圖書目錄,序言,
基本介紹
內容簡介
《機率論教程:英文版(第3版)》:經典原版書庫。
作者簡介
Kai Lai Chung（鐘開萊，1917-2009）華裔數學家、機率學家。浙江杭州人。1917年生於上海。1936年考入清華大學物理系。1940年畢業於西南聯合大學數學系，之後任西南聯合大學數學系助教。1944年考取第六屆庚子賠款公費留美獎學金。1945年底赴美國留學。1947年獲普林斯頓大學博士學位。20世紀50年代任教於美國紐約州Syracuse大學，60年代以後任史丹福大學數學系教授、系主任、名譽教授。鐘開萊著有十餘部專著。為世界公認的20世紀後半葉“機率學界學術教父”。
圖書目錄
Index
Preface to the third editioniii
Preface to the second editionv
Preface to the first editionvii
1 Distribution function
1.1 Monotone functionsl
1.2 Distribution functions
1.3 Absolutely continuous and singular distributions
2 Measure theory
2.1 Classes of sets
2.2 Probability measures and their distribution functions
3 Random variable. Expectation. Independence
3.1 General definitions
3.2 Properties of mathematical expectation
3.3 Independence
4 Convergence concepts
4.1 Various modes of convergence
4.2 Almost sure convergence; Borel-Cantelli lemma
4.3 Vague convergence
4.4 Continuation
4.5 Uniform integrability; convergence of moments
5 Law of large numbers. Random series
5.1 Simple limit theorems
5.2 Weak law of large numbers
5.3 Convergence of series
5.4 Strong law of large numbers
5.5 Applications
Bibliographical Note
6 Characteristic function
6.1 General properties; convolutions
6.2 Uniqueness and inversion
6.3 Convergence theorems
6.4 Simple applications
6.5 Representation theorems
6.6 Multidimensional case; Laplace transforms
Bibliographical Note
7 Central limit theorem and its ramifications
7.1 Liapounov's theorem
7.2 Lindeberg-FeUer theorem
7.3 Ramifications of the central limit theorem
7.4 Error estimation
7.5 Law of the iterated logarithm
7.6 Infinite divisibility
Bibliographical Note
8 Random walk
8.1 Zero-or-one laws
8.2 Basic notions
8.3 Recurrence
8.4 Fine structure
8.5 Continuation
Bibliographical Note
9 Conditioning. Markov property. Martingale
9.1 Basic properties of conditional expectation3 l
9.2 Conditional independence; Markov property
9.3 Basic properties of smartingales
9.4 Inequalities and convergence
9.5 Applications
Bibliographical Note
Supplement: Measure and Integral
1 Construction of measure
2 Characterization of extensions
3 Measures in R
4 Integral
5 Applications
General Bibliography
Preface to the third editioniii
Preface to the second editionv
Preface to the first editionvii
1 Distribution function
1.1 Monotone functionsl
1.2 Distribution functions
1.3 Absolutely continuous and singular distributions
2 Measure theory
2.1 Classes of sets
2.2 Probability measures and their distribution functions
3 Random variable. Expectation. Independence
3.1 General definitions
3.2 Properties of mathematical expectation
3.3 Independence
4 Convergence concepts
4.1 Various modes of convergence
4.2 Almost sure convergence; Borel-Cantelli lemma
4.3 Vague convergence
4.4 Continuation
4.5 Uniform integrability; convergence of moments
5 Law of large numbers. Random series
5.1 Simple limit theorems
5.2 Weak law of large numbers
5.3 Convergence of series
5.4 Strong law of large numbers
5.5 Applications
Bibliographical Note
6 Characteristic function
6.1 General properties; convolutions
6.2 Uniqueness and inversion
6.3 Convergence theorems
6.4 Simple applications
6.5 Representation theorems
6.6 Multidimensional case; Laplace transforms
Bibliographical Note
7 Central limit theorem and its ramifications
7.1 Liapounov's theorem
7.2 Lindeberg-FeUer theorem
7.3 Ramifications of the central limit theorem
7.4 Error estimation
7.5 Law of the iterated logarithm
7.6 Infinite divisibility
Bibliographical Note
8 Random walk
8.1 Zero-or-one laws
8.2 Basic notions
8.3 Recurrence
8.4 Fine structure
8.5 Continuation
Bibliographical Note
9 Conditioning. Markov property. Martingale
9.1 Basic properties of conditional expectation3 l
9.2 Conditional independence; Markov property
9.3 Basic properties of smartingales
9.4 Inequalities and convergence
9.5 Applications
Bibliographical Note
Supplement: Measure and Integral
1 Construction of measure
2 Characterization of extensions
3 Measures in R
4 Integral
5 Applications
General Bibliography
序言
In this new edition, I have added a Supplement on Measure and Integral. The subject matter is first treated in a general setting pertinent to an abstract measure space, and then specified in the classic Borel-Lebesgue case for the real line. The latter material, an essential part of real analysis, is presupposed in the original edition published in 1968 and revised in the second edition of 1974. When I taught the course under the title "Advanced Probability" at Stanford University beginning in 1962, students from the departments of statistics, operations research （formerly industrial engineering）, electrical engi- neering, etc. often had to take a prerequisite course given by other instructors before they enlisted in my course. In later years I prepared a set of notes, lithographed and distributed in the class, to meet the need. This forms the basis of the present Supplement. It is hoped that the result may as well serve in an introductory mode, perhaps also independently for a short course in the stated topics.
The presentation is largely self-contained with only a few particular refer- ences to the main text. For instance, after （the old） ~2.1 where the basic notions of set theory are explained, the reader can proceed to the first two sections of the Supplement for a full treatment of the construction and completion of a general measure; the next two sections contain a full treatment of the mathe- matical expectation as an integral, of which the properties are recapitulated in 3.2. In the final section, application of the new integral to the older Riemann integral in calculus is described and illustrated with some famous examples. Throughout the exposition, a few side remarks.
The presentation is largely self-contained with only a few particular refer- ences to the main text. For instance, after （the old） ~2.1 where the basic notions of set theory are explained, the reader can proceed to the first two sections of the Supplement for a full treatment of the construction and completion of a general measure; the next two sections contain a full treatment of the mathe- matical expectation as an integral, of which the properties are recapitulated in 3.2. In the final section, application of the new integral to the older Riemann integral in calculus is described and illustrated with some famous examples. Throughout the exposition, a few side remarks.
