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The Bayes Factor Versus Other Model Selection Criteria for the Selection of Constrained Models

  • Ming-Hui ChenEmail author
  • Sungduk Kim
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

Model assessment and model comparison are a crucial part of statistical analysis. Due to recent computational advances, sophisticated techniques for Bayesian model assessment are becoming increasingly popular. There is a rich literature on Bayesian methods for model assessment and model comparison, including [1, 3, 6, 9, 10, 13, 14, 16, 17, 18, 24, 26, 28, 30, 32, 33, 34, 36]. The scope of Bayesian model assessment can be investigated via model diagnostics, goodness of fit measures, or posterior model probabilities (or Bayes factors). A comprehensive account of model diagnostics and related methods for model assessment is given in [15].

Keywords

Posterior Distribution Marginal Likelihood Deviance Information Criterion Unconstrained Model Monte Carlo Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.Division of Epidemiology, Statistics and Prevention ResearchNational Institute of Child Health and Human Development, NIHRockvilleUSA

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