Bayesian Methods: A Social and Behavioral Sciences Approach, Second Edition

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CRC Press, Nov 26, 2007 - Mathematics - 752 pages
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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.

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    This book isn't only for social scientists. It is an excellent and clear explaination of Bayesian Inference, useful for all scientists and engineers.
    Instead of presenting the reader with pages
    of equations and a few sparse sentences of text, Gill presents a key equations, such as Bayes Rule and then devotes paragraphs of well written text to explain the meaning and signifcance of the equations.
    The book also includes a lovely worked examples, including an example of a Bayes Markov chain.
    I found this invaluable during my PhD in computer science.

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