Probability and Measure Theory

AvRobert B. Ash,Catherine A. Doleans-Dade

Inbunden, Engelska, 1999

1 128 kr

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Beskrivning

Probability and Measure Theory, Second Edition, is a text for a graduate-level course in probability that includes essential background topics in analysis. It provides extensive coverage of conditional probability and expectation, strong laws of large numbers, martingale theory, the central limit theorem, ergodic theory, and Brownian motion.

Clear, readable style
Solutions to many problems presented in text
Solutions manual for instructors
Material new to the second edition on ergodic theory, Brownian motion, and convergence theorems used in statistics
No knowledge of general topology required, just basic analysis and metric spaces
Efficient organization

Produktinformation

Utgivningsdatum:1999-12-08
Mått:152 x 229 x 37 mm
Vikt:990 g
Format:Inbunden
Språk:Engelska
Antal sidor:528
Upplaga:2
Förlag:Elsevier Science
ISBN:9780120652020

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik

Mer om författaren

Robert B. Ash as written about, taught, or studied virtually every area of mathematics. His books include Information Theory, Topics in Stochastic Processes, The Calculus Tutoring Book, Introduction to Discrete Mathematics, and A Primer of Mathematics.

Recensioner i media

"There are numerous probability texts on the market, which makes choosing one difficult. If you are a financial professional who knows basic probability theory, but wants to take the next step in sophistication, this is the essential text. It introduces basic measure theory and functional analysis, and then delves into probability. The writing is clear and highly accessible. The choice of topics is perfect for financial engineers or financial risk managers: martingales, the inversion theorem, the central limit theorem, Brownian motion and stochastic integrals. I can't praise this book enough. It is exceptional!" --http://www.contingencyanalysis.com

Innehållsförteckning

Summary of NotationFundamentals of Measure and Integration TheoryFurther Results in Measure and Integration TheoryIntroduction to Functional AnalysisBasic Concepts of ProbabilityConditional Probability and ExpectationStrong Laws of Large Numbers and Martingale TheoryThe Central Limit TheoremErgodic TheoryBrownian Motion and Stochastic Integrals