Objective Bayesian methods for model selection: introduction and
J. Berger and L.R. Pericchi
The basics of the Bayesian approach to model selection are first
presented, as well as the motivations for the Bayesian approach. We
then review four methods of developing default Bayesian
procedures that have undergone considerable recent development, the
Conventional Prior approach, the Bayes Information Criterion,
the Intrinsic Bayes Factor, and the Fractional Bayes
Factor. As part of the review, these methods
are illustrated on examples involving the normal linear model.
The later part of the chapter focuses on comparison of the
four approaches, and includes an extensive discussion of criteria
for judging model selection procedures.
As the chapter is lengthy, we include here an index to the sections.
Postscript File (484 kB)