Measure and Probability
Autor Athreya Siva, Siva Athreya, V. S. Sunderen Limba Engleză Hardback – 31 dec 2008
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Specificații
ISBN-13: 9781439801260
ISBN-10: 1439801266
Pagini: 232
Dimensiuni: 156 x 235 mm
Greutate: 0.5 kg
Ediția:New.
Editura: CRC Press
ISBN-10: 1439801266
Pagini: 232
Dimensiuni: 156 x 235 mm
Greutate: 0.5 kg
Ediția:New.
Editura: CRC Press
Public țintă
Statisticians.Cuprins
Probabilities and Measures
Introduction
σ-algebras as events
Algebras, monotone classes, etc.
Preliminaries on measures
Outer measures and Caratheodory extension
Lebesgue measure
Regularity
Bernoulli trials
Integration
Measurable functions
Integration
a.e. considerations
Random Variables
Distribution and expectation
Independent events and tail σ-algebra
Some distributions
Conditional expectation
Probability Measures on Product Spaces
Product measures
Joint distribution and independence
Probability measures on infinite product spaces
Kolmogorov consistency theorem
Characteristics and Convergences
Characteristic functions
Modes of convergence
Central limit theorem
Law of large numbers
Markov Chains
Discrete time MC
Examples
Classification of states
Strong Markov property
Stationary distribution
Limit theorems
Some Analysis
Complex measures
Lp spaces
RadonߝNikodym theorem
Change of variables
Differentiation
The Riesz representation theorem
Appendix
Metric spaces
Topological spaces
Compactness
The StoneߝWeierstrass theorem
Tables
References
Index
Introduction
σ-algebras as events
Algebras, monotone classes, etc.
Preliminaries on measures
Outer measures and Caratheodory extension
Lebesgue measure
Regularity
Bernoulli trials
Integration
Measurable functions
Integration
a.e. considerations
Random Variables
Distribution and expectation
Independent events and tail σ-algebra
Some distributions
Conditional expectation
Probability Measures on Product Spaces
Product measures
Joint distribution and independence
Probability measures on infinite product spaces
Kolmogorov consistency theorem
Characteristics and Convergences
Characteristic functions
Modes of convergence
Central limit theorem
Law of large numbers
Markov Chains
Discrete time MC
Examples
Classification of states
Strong Markov property
Stationary distribution
Limit theorems
Some Analysis
Complex measures
Lp spaces
RadonߝNikodym theorem
Change of variables
Differentiation
The Riesz representation theorem
Appendix
Metric spaces
Topological spaces
Compactness
The StoneߝWeierstrass theorem
Tables
References
Index
Recenzii
This textbook is suitable for a one-semester course on measure theory and probability for beginning graduate students in mathematics, probability and statistics. It can also be used as a textbook for advanced undergraduate students in mathematics … The topics are well selected to meet the needs of students who are interested in graduate studies in areas related to analysis, probability, stochastic processes and statistics … This makes the book student-friendly. A motivated student can use it by him- or herself to learn the topics well.
—Yimin Xiao, Mathematical Reviews, 2010
—Yimin Xiao, Mathematical Reviews, 2010
Descriere
Covering the fundamentals of measure theory and probability theory, this title begins with the construction of Lebesgue measure via Caratheodory's outer measure approach and goes on to discuss integration and standard convergence theorems.