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  <titleInfo>
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    <title>introduction to measure-theoretic probability</title>
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  <name type="personal">
    <namePart>Roussas, George G.</namePart>
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    <dateIssued encoding="marc">2014</dateIssued>
    <edition>2nd ed.</edition>
    <issuance>monographic</issuance>
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    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <abstract>"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"--</abstract>
  <tableOfContents>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.</tableOfContents>
  <note type="statement of responsibility">by George G. Roussas.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Probabilities</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Measure theory</topic>
  </subject>
  <subject authority="fast">
    <topic>Measure theory</topic>
  </subject>
  <subject authority="fast">
    <topic>Probabilities</topic>
  </subject>
  <classification authority="lcc">QA273 .R864 2014eb</classification>
  <classification authority="ddc" edition="23">519.2</classification>
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      <title>Introduction to measure-theoretic probability</title>
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    <name>
      <namePart>Roussas, George G.</namePart>
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      <edition>Second edition</edition>
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    <identifier type="local">(DLC)  2014007243</identifier>
    <identifier type="local">(OCoLC)868642456</identifier>
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  <identifier type="isbn">9780128002902</identifier>
  <identifier type="isbn">0128002905</identifier>
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