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dc.contributor.authorGreen, Peter*
dc.contributor.authorLatuszyski, Krzysztof*
dc.contributor.authorPereyra, Marcelo*
dc.contributor.authorRobert, Christian P.*
dc.date.accessioned2015-02-27T10:55:40Z
dc.date.available2015-02-27T10:55:40Z
dc.date.issued2015
dc.identifier.issn0960-3174
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/14720
dc.language.isoenen
dc.subjectBayesian analysis
dc.subjectoptimisation
dc.subjectABC techniques
dc.subjectMCMC algorithms
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.titleBayesian computation: a perspective on the current state, and sampling backwards and forwards
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherIUF - Institut Universitaire de France;France
dc.contributor.editoruniversityotherCREST - Centre de Recherche en Économie et Statistique;France
dc.contributor.editoruniversityotherUniversity of Warwick;Royaume-Uni
dc.contributor.editoruniversityotherUniversity of Bristol;Royaume-Uni
dc.description.abstractenThe past decades have seen enormous im-provements in computational inference based on sta-tistical models, with continual enhancement in a wide range of computational tools, in competition. In Bayesian inference, first and foremost, MCMC techniques con-tinue to evolve, moving from random walk proposals to Langevin drift, to Hamiltonian Monte Carlo, and so on, with both theoretical and algorithmic inputs opening wider access to practitioners. However, this impressive evolution in capacity is confronted by an even steeper increase in the complexity of the models and datasets to be addressed. The difficulties of modelling and then handling ever more complex datasets most likely call for a new type of tool for computational inference that dramatically reduce the dimension and size of the raw data while capturing its essential aspects. Approximate models and algorithms may thus be at the core of the next computational revolution.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameStatistics and Computing
dc.relation.isversionofjnlvol25
dc.relation.isversionofjnlissue4
dc.relation.isversionofjnldate2015
dc.relation.isversionofjnlpages835-862
dc.relation.isversionofdoi10.1007/s11222-015-9574-5
dc.identifier.urlsitehttps://arxiv.org/abs/1502.01148v3
dc.relation.isversionofjnlpublisherChapman & Hall
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.submittednonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2016-10-07T14:53:03Z
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