Measure Theory and Probability
Adams, Malcolm R.; Guillemin, Victor
Language: English
Published by Birkhauser Boston Inc, 1996
- Hardcover
- New
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
AbeBooks seller since February 27, 2001
Condition: New
£ 98.38
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Add to basketItem description from seller
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. Series: The Wadsworth & Brooks/Cole Mathematics Series (Closed). Num Pages: 206 pages, biography. BIC Classification: PBT. Category: (UU) Undergraduate. Dimension: 241 x 165 x 20. Weight in Grams: 490. . 1996. 1996th Edition. Hardcover. . . . .
Seller Inventory # V9780817638849
- Title
- Measure Theory and Probability
- Author
- Adams, Malcolm R.; Guillemin, Victor
- Publisher
- Birkhauser Boston Inc
- Publication year
- 1996
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0817638849
- ISBN 13
- 9780817638849
"…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 exercises; helpful hints or solution outlines are given with many of the problems. All in all, the text should make a useful reference for professionals and students."―The Journal of the American Statistical Association
"Synopsis" may belong to another edition of this title.
From the Back Cover
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 Szegö theorem and the central limit theorem. In this concise text, quite a few applications to probability are packed into the exercises.
"…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 exercises; helpful hints or solution outlines are given with many of the problems. All in all, the text should make a useful reference for professionals and students."―The Journal of the American Statistical Association
"About the title" may belong to another edition of this title.
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AbeBooks seller since February 27, 2001
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