Generalization of Jeffreys' divergence based priors for Bayesian hypothesis testing

Details Generalization of Jeffreys' divergence based priors for Bayesian hypothesis testing Generalization of Jeffreys' divergence based priors for Bayesian hypothesis testing

2008-01-28

arxiv.org/abs/0801.4224v1

Journal of the Royal Statistical Society, Series B, (2008), vol. 70, pp. 981--1003

Statistics

Methodology

Scientific paper

In this paper we introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence based (DB) priors. DB priors have simple forms and desirable properties, like information (finite sample) consistency; often, they are similar to other existing proposals like the intrinsic priors; moreover, in normal linear models scenarios, they exactly reproduce Jeffreys-Zellner-Siow priors. Most importantly, in challenging scenarios such as irregular models and mixture models, the DB priors are well defined and very reasonable, while alternative proposals are not. We derive approximations to the DB priors as well as MCMC and asymptotic expressions for the associated Bayes factors.

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