The intrinsic bayes factor for model selection and prediction

Published

Journal Article

In the Bayesian approach to model selection or hypothesis testing with models or hypotheses of differing dimensions, it is typically not possible to utilize standard noninformative (or default) prior distributions. This has led Bayesians to use conventional proper prior distributions or crude approximations to Bayes factors. In this article we introduce a new criterion called the intrinsic Bayes factor, which is fully automatic in the sense of requiring only standard noninformative priors for its computation and yet seems to correspond to very reasonable actual Bayes factors. The criterion can be used for nested or nonnested models and for multiple model comparison and prediction. From another perspective, the development suggests a general definition of a “reference prior” for model comparison. Copyright 1996 Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Berger, JO; Pericchi, LR

Published Date

  • March 1, 1996

Published In

Volume / Issue

  • 91 / 433

Start / End Page

  • 109 - 122

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.1996.10476668

Citation Source

  • Scopus