Measure theory and probability

著者： Malcolm Ritchie Adams; Victor Guillemin Boston : Birkhäuse, ©1996. 打印图书 : 英语查看所有的版本和格式 WorldCat "Measure theory and integration are presented to undergraduates from the perspective of probability theory. The first chapter shows why measure theory is needed for the formulation of problems in probability, and explains why one would have been forced to invent Lebesgue theory (had it not already existed) to contend with the paradoxes of large numbers. The measure-theoretic approach then leads to interesting applications and a range of topics that include the construction of the Lebesgue measure on R [superscript n] (metric space approach), the Borel-Cantelli lemmas, straight measure theory (the Lebesgue integral). Chapter 3 expands on abstract Fourier analysis, Fourier series and the Fourier integral, which have some beautiful probabilistic applications: Polya's theorem on random walks, Kac's proof of the Szego theorem and the central limit theorem. In this concise text, quite a few applications to probability are packed into the exercises."--Jacket.  再读一些... (尚未评估) 0 附有评论 - 争取成为第一个。

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材料类型： 互联网资源 图书, 互联网资源 Malcolm Ritchie Adams; Victor Guillemin 查找更多有关下列内容的信息： Malcolm Ritchie Adams Victor Guillemin 0817638849 9780817638849 3764338849 9783764338848 33668134 xiv, 205 pages : illustrations ; 24 cm Measure theory -- Integration -- Fourier analysis. Malcolm Adams, Victor Guillemin.

摘要：

Suitable for instructors and students of statistical measure theoretic courses, this title features numerous informative exercises, and helpful hints or solution outlines with many of the problems.  再读一些...

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"...the text is user friendly to the topics it considers and should be very accessible...Instructors and students of statistical measure theoretic courses will appreciate the numerous informative 再读一些...

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schema:reviewBody ""Measure theory and integration are presented to undergraduates from the perspective of probability theory. The first chapter shows why measure theory is needed for the formulation of problems in probability, and explains why one would have been forced to invent Lebesgue theory (had it not already existed) to contend with the paradoxes of large numbers. The measure-theoretic approach then leads to interesting applications and a range of topics that include the construction of the Lebesgue measure on R [superscript n] (metric space approach), the Borel-Cantelli lemmas, straight measure theory (the Lebesgue integral). Chapter 3 expands on abstract Fourier analysis, Fourier series and the Fourier integral, which have some beautiful probabilistic applications: Polya's theorem on random walks, Kac's proof of the Szego theorem and the central limit theorem. In this concise text, quite a few applications to probability are packed into the exercises."--Jacket." ;
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