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Measure theory and probability

Autor: Malcolm Ritchie Adams; Victor Guillemin
Editora: Boston : Birkhäuse, ©1996.
Edição/Formato   Imprimir livro : InglêsVer todas as edições e formatos
Resumo:
"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  Ler mais...
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Detalhes

Tipo de Material: Recurso Internet
Tipo de Documento Livro, Recursos de internet
Todos os Autores / Contribuintes: Malcolm Ritchie Adams; Victor Guillemin
ISBN: 0817638849 9780817638849 3764338849 9783764338848
Número OCLC: 33668134
Descrição: xiv, 205 pages : illustrations ; 24 cm
Conteúdos: Measure theory --
Integration --
Fourier analysis.
Responsabilidade: Malcolm Adams, Victor Guillemin.

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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  Ler mais...

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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 Ler mais...

 
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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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