Show simple item record

ABC likelihood-free methods for model choice in Gibbs random fields

dc.contributor.authorGrelaud, Aude
dc.contributor.authorRobert, Christian P.
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
dc.contributor.authorRodolphe, François
dc.contributor.authorTaly, Jean-François
dc.date.accessioned2010-01-18T09:42:01Z
dc.date.available2010-01-18T09:42:01Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/2998
dc.language.isoenen
dc.subjectStatistical modelsen
dc.subjectMarkoven
dc.subject.ddc519en
dc.titleABC likelihood-free methods for model choice in Gibbs random fieldsen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherINRIA, Paris Sud, Paris;France
dc.contributor.editoruniversityotherINRA;France
dc.contributor.editoruniversityotherÉcole Nationale de la Statistique et de l'Administration Économique, Paris;France
dc.description.abstractenGibbs random fields are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard model choice methods is hampered by the unavailability of the normalising constant in the Gibbs likelihood. In particular, from a Bayesian perspective, the computation of the posterior probabilities of the models under competition requires special likelihood-free simulation techniques like the Approximate Bayesian Computation (ABC) algorithm that is intensively used in population Genetics. We show in this paper how to implement an ABC algorithm geared towards model choice in the general setting of Gibbs random fields, demonstrating in particular that there exists a sufficient statistic across models. The accuracy of the approximation to the posterior probabilities can be further improved by importance sampling on the distribution of the models. The practical aspects of the method are detailed through two applications, the test of an iid Bernoulli model versus a first-order Markov chain, and the choice of a folding structure for a protein of Thermotoga maritima implicated into signal transduction processes.en
dc.relation.isversionofjnlnameBayesian Analysis
dc.relation.isversionofjnlvol4
dc.relation.isversionofjnlissue2
dc.relation.isversionofjnldate2009
dc.relation.isversionofjnlpages317-336
dc.relation.isversionofdoihttp://dx.doi.org/10.1214/09-BA412
dc.identifier.urlsitehttp://arxiv.org/abs/0807.2767v3
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherInternational Society for Bayesian Analysis
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record