Probability theory : independence, interchangeability, martingales

Yuan Shih Chow, Henry Teicher

This book is an introductory text on the beginning graduate level. Its subjects are the measure theoretical foundation of the theory and the main laws and theorems which emerge therefrom. The authors concentrate on certain important topics, primarily independence, interchangeability, and martingales. Particular emphasis is placed on stopping times, as tools in proving theorems as well as objects of interest in themselves. Among their applications is renewal theory; another useful application explained in this book is connected with the limiting behavior of random walks. A knowledge of measure theory is not assumed by the authors who intertwine measure and probability in their presentation as opposed to the customary sharp demarcation. The book can, however, be used as a text for students who have already been exposed to a course in measure theory. Many examples and exercises accompany the text.

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  • 1 Classes of Sets, Measures, and Probability Spaces.- 1.1 Sets and set operations.- 1.2 Spaces and indicators.- 1.3 Sigma-algebras, measurable spaces, and product spaces.- 1.4 Measurable transformations.- 1.5 Additive set functions, measures, and probability spaces.- 1.6 Induced measures and distribution functions.- 2 Binomial Random Variables.- 2.1 Poisson theorem, interchangeable events, and their limiting probabilities.- 2.2 Bernoulli, Borel theorems.- 2.3 Central limit theorem for binomial random variables, large deviations.- 3 Independence.- 3.1 Independence, random allocation of balls into cells.- 3.2 Borel-Cantelli theorem, characterization of independence, Kolmogorov zero-one law.- 3.3 Convergence in probability, almost certain convergence, and their equivalence for sums of independent random variables.- 3.4 Bernoulli trials.- 4 Integration in a Probability Space.- 4.1 Definition, properties of the integral, monotone convergence theorem.- 4.2 Indefinite integrals, uniform integrability, mean convergence.- 4.3 Jensen, Holder, Schwarz inequalities.- 5 Sums of Independent Random Variables.- 5.1 Three series theorem.- 5.2 Laws of large numbers.- 5.3 Stopping times, copies of stopping times, Wald's equation.- 5.4 Chung-Fuchs theorem, elementary renewal theorem, optimal stopping.- 6 Measure Extensions, Lebesgue-Stieltjes Measure, Kolmogorov Consistency Theorem.- 6.1 Measure extensions, Lebesgue-Stieltjes measure.- 6.2 Integration in a measure space.- 6.3 Product measure, Fubini's theorem, n-dimensional Lebesgue-Stieltjes measure.- 6.4 Infinite-dimensional product measure space, Kolmogorov consistency theorem.- 6.5 Absolute continuity of measures, distribution functions
  • Radon-Nikodym theorem.- 7 Conditional Expectation, Conditional Independence, Introduction to Martingales.- 7.1 Conditional expectations.- 7.2 Conditional probabilities, conditional probability measures.- 7.3 Conditional independence, interchangeable random variables.- 7.4 Introduction to martingales.- 8 Distribution Functions and Characteristic Functions.- 8.1 Convergence of distribution functions, uniform integrability, Helly-Bray theorem.- 8.2 Weak compactness, Frechet-Shohat, Glivenko- Cantelli theorems.- 8.3 Characteristic functions, inversion formula, Levy continuity theorem.- 8.4 The nature of characteristic functions, analytic characteristic functions, Cramer-Levy theorem.- 8.5 Remarks on k-dimensional distribution functions and characteristic functions.- 9 Central Limit Theorems.- 9.1 Independent components.- 9.2 Interchangeable components.- 9.3 The martingale case.- 9.4 Miscellaneous central limit theorems.- 9.5 Central limit theorems for double arrays.- 10 Limit Theorems for Independent Random Variables.- 10.1 Laws of large numbers.- 10.2 Law of the iterated logarithm.- 10.3 Marcinkiewicz-Zygmund inequality, dominated ergodic theorems.- 10.4 Maxima of random walks.- 11 Martingales.- 11.1 Upcrossing inequality and convergence.- 11.2 Martingale extension of Marcinkiewicz-Zygmund inequalities.- 11.3 Convex function inequalities for martingales.- 11.4 Stochastic inequalities.- 12 Infinitely Divisible Laws.- 12.1 Infinitely divisible characteristic functions.- 12.2 Infinitely divisible laws as limits.- 12.3 Stable laws.

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書名 Probability theory : independence, interchangeability, martingales
著作者等 Chow, Yuan Shih
Teicher, Henry
Chow Y S
Teicher H
シリーズ名 Springer texts in statistics
出版元 Springer-Verlag
刊行年月 c1988
版表示 2nd ed
ページ数 xviii, 467 p.
大きさ 25 cm
ISBN 3540966951
NCID BA04298633
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言語 英語
出版国 アメリカ合衆国