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Bayesian Missing Data Problems

EM, Data Augmentation and Noniterative Computation

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Paperback / softback
04 November 2019
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This book presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors, based on the inverse Bayes formulae. The authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. They describe Monte Carlo simulation, numerical techniques, and optimization methods. The book illustrates the methods with biostatistical models and real-world applications, including mixed effects and hierarchical models, nonresponse and contingency tables, and the constrained parameter problem reformulated as a missing data problem.

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    Product Details
    ISBN-13
    9780367385309
    Author
    Ming T. Tan
    Publisher
    Taylor & Francis Ltd
    Published
    04 November 2019
    Format
    Paperback / softback

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    $184.79
    Ships in 3-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Bayesian Missing Data Problems

    $184.79

    Description

    This book presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors, based on the inverse Bayes formulae. The authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. They describe Monte Carlo simulation, numerical techniques, and optimization methods. The book illustrates the methods with biostatistical models and real-world applications, including mixed effects and hierarchical models, nonresponse and contingency tables, and the constrained parameter problem reformulated as a missing data problem.

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