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Bayesian Methods : A Social and Behavioral Sciences Approach

Bayesian Methods : A Social and Behavioral Sciences Approach

Bayesian Methods : A Social and Behavioral Sciences Approach Hardback - 2007

by Jeff Gill

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CRC Press LLC, 2007. Hardcover. Very Good. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.Dust jacket quality is not guaranteed.
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Details

  • Title Bayesian Methods : A Social and Behavioral Sciences Approach
  • Author Jeff Gill
  • Binding Hardback
  • Edition [ Edition: secon
  • Condition Used - Very good
  • Pages 711
  • Volumes 1
  • Language ENG
  • Publisher CRC Press LLC, London
  • Publication date 2007
  • Illustrated Yes
  • Bookseller's Inventory # G1584885629I4N00
  • ISBN 9781584885627 / 1584885629
  • Weight 2.56 lbs (1.16 kg)
  • Dimensions 9.29 x 6.44 x 1.7 in (23.60 x 16.36 x 4.32 cm)
  • Category Sociology
  • Library of Congress subjects Bayesian statistical decision theory, Social sciences - Statistical methods
  • Library of Congress Catalogue Number 2007025535
  • Dewey Decimal Code 519.542
  • Quantity available 1

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Reader reviews for Bayesian Methods : A Social and Behavioral Sciences Approach

From the publisher

The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings.

New to the Second Edition

  • Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling
  • Expanded coverage of Bayesian linear and hierarchical models
  • More technical and philosophical details on prior distributions
  • A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals

    Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.

  • First line

    Statistical analysis is the process of separating out systematic effects from the random noise inherent in all sets of observations.
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