Basic Elements of Bayesian Statistics

  • Jean-Paul Fox
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)


A review of Bayesian estimation and testing methods is given that is not a thorough overview but concentrates on some speci_c elements. First, simulation-based methods for parameter estimation, like the Gibbs sampling and the Metropolis-Hastings algorithms, from the general class of Markov chain Monte Carlo algorithms, are discussed. Second, the Bayesian approach to model selection and hypothesis testing is presented. The techniques and methods described in this chapter are needed to completely exploit the Bayesian machinery for item response modeling.


Markov Chain Monte Carlo Posterior Density Marginal Likelihood Deviance Information Criterion Markov Chain Monte Carlo Method 
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Copyright information

© Springer New York 2010

Authors and Affiliations

  • Jean-Paul Fox
    • 1
  1. 1.Department of Research Methodology, Measurement, and Data Analysis Faculty of Behavioral SciencesUniversity of TwenteEnschedeThe Netherlands

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