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dc.contributor.authorPillai, Natesh S.
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
dc.contributor.authorRobert, Christian P.
dc.subjectBayes factoren
dc.subjectmodel choiceen
dc.titleWhy approximate Bayesian computational (ABC) methods cannot handle model choice problemsen
dc.typeDocument de travail / Working paper
dc.contributor.editoruniversityotherDepartment of Statistics, Harvard University Harvard university (Cambridge, USA);États-Unis
dc.contributor.editoruniversityotherInstitut de Mathématiques et de Modélisation de Montpellier (I3M) CNRS : UMR5149 – Université Montpellier II - Sciences et Techniques du Languedoc;France
dc.contributor.editoruniversityotherInstitut Universitaire de France (IUF) Ministère de l'Enseignement Supérieur et de la Recherche Scientifique;France
dc.contributor.editoruniversityotherCentre de Recherche en Économie et Statistique (CREST) INSEE – École Nationale de la Statistique et de l'Administration Économique;France
dc.description.abstractenApproximate Bayesian computation (ABC), also known as likelihood-free methods, have become a favourite tool for the analysis of complex stochastic models, primarily in population genetics but also in financial analyses. We advocated in Grelaud et al. (2009) the use of ABC for Bayesian model choice in the specific case of Gibbs random fields (GRF), relying on a sufficiency property mainly enjoyed by GRFs to show that the approach was legitimate. Despite having previously suggested the use of ABC for model choice in a wider range of models in the DIY ABC software (Cornuet et al., 2008), we present theoretical evidence that the general use of ABC for model choice is fraught with danger in the sense that no amount of computation, however large, can guarantee a proper approximation of the posterior probabilities of the models under comparison.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen

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