Generalization of Jeffreys' divergence based priors for Bayesian hypothesis testing

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dc.creatorBayarri, M. J.
dc.creatorGarcía-Donato, G.
dc.date2008-01-28
dc.date.accessioned2026-07-07T12:46:53Z
dc.date.available2026-07-07T12:46:53Z
dc.descriptionIn 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.
dc.identifierhttps://arxiv.org/abs/0801.4224
dc.identifierhttp://arxiv.org/abs/0801.4224
dc.identifierJournal of the Royal Statistical Society, Series B, (2008), vol. 70, pp. 981--1003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/221533
dc.subjectMethodology
dc.titleGeneralization of Jeffreys' divergence based priors for Bayesian hypothesis testing
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