Mixtures of g priors for Bayesian variable selection

Feng Liang, Rui Paulo, German Molina, Merlise A. Clyde, Jim O. Berger

Research output: Contribution to journalArticlepeer-review


Zellner's g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the problems with the original formulation while maintaining the computational tractability that has made the g prior so popular. We present theoretical properties of the mixture g priors and provide real and simulated examples to compare the mixture formulation with fixed g priors, empirical Bayes approaches, and other default procedures.

Original languageEnglish (US)
Pages (from-to)410-423
Number of pages14
JournalJournal of the American Statistical Association
Issue number481
StatePublished - Mar 2008


  • AIC
  • BIC
  • Bayesian model averaging
  • Cauchy
  • Empirical bayes
  • Gaussian hypergeometric functions
  • Model selection
  • Zellner-Siow priors

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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