Measure Theory and Probability

Description

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. --back cover

Subjects

Other Editions

• Measure Theory and ProbabilityHardcoverBirkhäuser1996-01-01
• Measure theory and probabilityWadsworth & Brooks/Cole Advanced Books and Software1986-01-01
• Measure Theory and ProbabilityBirkhäuser Boston1995-01-01
• Measure Theory and ProbabilityPaperbackBirkhäuser1997-02-09
• Measure Theory and ProbabilityBirkhauser Verlag2013-01-01
• Measure Theory and ProbabilityPaperbackBirkhäuser2014-10-18