概率論教程：英文版（第3版） [A Course In Probability Theory] 下載 mobi epub pdf 電子書 2024

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內容簡介

隨機變量和分布函數，測度論，數學期望，方差，各種收斂性，大數律， 中心極限定理，特徵函數，隨機遊動， 馬氏性和鞅理論.本書內容豐富，邏輯緊密，敘述嚴謹，不僅可以擴展讀者的視野，而且還將為其後續的學習和研究打下堅實基礎。此外，本書的習題較多， 都經過細心的遴選， 從易到難， 便於讀者鞏固練習。本版補充瞭有關測度和積分方麵的內容，並增加瞭一些習題。
本書是一本享譽世界的經典概率論教材，令眾多讀者受益無窮，自齣版以來，已被世界75%以上的大學的數萬名學生使用。本書內容豐富，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，本書的習題較多， 都經過細心的遴選, 從易到難, 便於讀者鞏固練習。本版補充瞭有關測度和積分方麵的內容，並增加瞭一些習題。

作者簡介

Kai Lai Chung（鍾開萊，1917-2009）華裔數學傢、概率學傢。浙江杭州人。1917年生於上海。1936年考入清華大學物理係。1940年畢業於西南聯閤大學數學係，之後任西南聯閤大學數學係助教。1944年考取第六屆庚子賠款公費留美奬學金。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年代以後任斯坦福大學數學係教授、係主任、名譽教授。鍾開萊著有十餘部專著。為世界公認的20世紀後半葉“概率學界學術教父”。

內頁插圖

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 Liapounovs 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

前言/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.



概率論教程：英文版（第3版） [A Course In Probability Theory] 下載 mobi epub pdf txt 電子書 格式

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是正版書，印刷的質量非常好。

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評分☆☆☆☆☆

《概率論教程:英文版(第3版)》是一本享譽世界的經典概率論教材，令眾多讀者受益無窮。自齣版以來。已被世界75％以上的大學的數萬名學生使用。《概率論教程: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，《概率論教程: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。本版補充瞭有關測度和積分方麵的內容，並增加瞭一些習題。這是鍾先生影響好幾代人的教材，印象中看過的不少東西裏麵都提到這本，印象中還有Billingsley的那本弱收斂的，Hall和Hedye那本鞅的，都是常能在文獻中看到被引用的教材。這本書看過但沒仔細看過，一點小小的感覺是不適閤入門，有些基礎看比較好，或拿當做參考吧。我看的是那種年代超級久遠的小薄本，紙張都是泛黃的，也不知道是第幾版，也許這本書後來做瞭大修吧，不知道瞭，我的信息集不完備。這是一本非常經典的概率論教材，內容豐富，寫作非常嚴謹，幾乎所有的定理都給齣瞭易於理解的證明，而且本書還精選瞭許多習題。但是本書並不適閤作為概率論的入門教材，在斯坦福大學也是作為研究生教材使用，因此要想更好地理解書中的內容，需要你具有一定的測度論基礎。

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著名數學傢寫的概率論，買來讀讀以開開眼界！

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