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An introduction to measure-theoretic probability /

"In this introductory chapter, the concepts of a field and of a [sigma]-field are introduced, they are illustrated bymeans of examples, and some relevant basic results are derived. Also, the concept of a monotone class is defined and its relationship to certain fields and [sigma]-fields is inve...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Roussas, George G. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; New York : Academic Press, an imprint of Elsevier, 2014.
Edición:Second edition.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Roussas, George G.,  |e author. 
245 1 3 |a An introduction to measure-theoretic probability /  |c by George G. Roussas. 
250 |a Second edition. 
264 1 |a Amsterdam ;  |a New York :  |b Academic Press, an imprint of Elsevier,  |c 2014. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
520 |a "In this introductory chapter, the concepts of a field and of a [sigma]-field are introduced, they are illustrated bymeans of examples, and some relevant basic results are derived. Also, the concept of a monotone class is defined and its relationship to certain fields and [sigma]-fields is investigated. Given a collection of measurable spaces, their product space is defined, and some basic properties are established. The concept of a measurable mapping is introduced, and its relation to certain [sigma]-fields is studied. Finally, it is shown that any random variable is the pointwise limit of a sequence of simple random variables"--  |c Provided by publisher 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a Certain classes of sets, measurability, and pointwise approximation -- Definition and construction of a measure and its basic properties -- Some modes of convergence of sequences of random variables and their relationships -- The integral of a random variable and its basic properties -- Standard convergence theorems, the Fubini theorem -- Standard moment and probability inequalities, convergence in the rth mean and its implications -- The Hahn-Jordan decomposition theorem, the Lebesgue decomposition theorem, and the Radon-Nikodym theorem -- Distribution functions and their basic properties, Helly-Bray type results -- Conditional expectation and conditional probability, and related properties and results -- Independence -- Topics from the theory of characteristic functions -- The central limit problem: the centered case -- The central limit problem: the noncentered case -- Topics from sequences of independent random variables -- Topics from Ergodic theory -- Two cases of statistical inference: estimation of a real-valued parameter, nonparametric estimation of a probability density function -- Appendixes: A. Brief review of chapters 1-16 -- B. Brief review of Riemann-Stieltjes integral -- C. Notation and abbreviations. 
650 0 |a Probabilities. 
650 0 |a Measure theory. 
650 6 |a Probabilit�es.  |0 (CaQQLa)201-0011592 
650 6 |a Th�eorie de la mesure.  |0 (CaQQLa)201-0005696 
650 7 |a probability.  |2 aat  |0 (CStmoGRI)aat300055653 
650 7 |a Measure theory  |2 fast  |0 (OCoLC)fst01013175 
650 7 |a Probabilities  |2 fast  |0 (OCoLC)fst01077737 
776 0 8 |i Print version:  |a Roussas, George G.  |t Introduction to measure-theoretic probability.  |b Second edition  |z 9780128000427  |w (DLC) 2014007243  |w (OCoLC)868642456 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128000427  |z Texto completo