Measure Theory and Probability

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 ex...

Full description

Bibliographic Details
Main Authors: Adams, Malcolm, Guillemin, Victor (Author)
Format: eBook
Language:English
Published: Boston, MA Birkhäuser 1996, 1996
Edition:1st ed. 1996
Series:The Wadsworth & Brooks/Cole Mathematics Series
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
LEADER 02540nmm a2200313 u 4500
001 EB000618303
003 EBX01000000000000000471385
005 00000000000000.0
007 cr|||||||||||||||||||||
008 140122 ||| eng
020 |a 9781461207795 
100 1 |a Adams, Malcolm 
245 0 0 |a Measure Theory and Probability  |h Elektronische Ressource  |c by Malcolm Adams, Victor Guillemin 
250 |a 1st ed. 1996 
260 |a Boston, MA  |b Birkhäuser  |c 1996, 1996 
300 |a XVI, 206 p  |b online resource 
505 0 |a 1 Measure Theory -- 2 Integration -- 3 Fourier Analysis -- Appendix A Metric Spaces -- Appendix C A Non-Measurable Subset of the Interval (0, 1] -- References 
653 |a Measure theory 
653 |a Probability Theory 
653 |a Measure and Integration 
653 |a Probabilities 
700 1 |a Guillemin, Victor  |e [author] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b SBA  |a Springer Book Archives -2004 
490 0 |a The Wadsworth & Brooks/Cole Mathematics Series 
028 5 0 |a 10.1007/978-1-4612-0779-5 
856 4 0 |u https://doi.org/10.1007/978-1-4612-0779-5?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 515.42 
520 |a 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