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Bayesian Methods for Statistical Analysis

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Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.
Paperback / softback
698 Pages
01-October-2015
RRP: $164.95
$162.48
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Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.

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    Product Details
    ISBN-13
    9781921934254
    Author
    Borek Puza
    Publisher
    ANU Press
    Published
    01-October-2015
    Format
    Paperback / softback
    Pages
    698

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    RRP: $164.95
    $162.48
    Ships in 3-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Bayesian Methods for Statistical Analysis

    RRP: $164.95
    $162.48

    Description

    Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.

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