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dc.contributor.authorBornn, Luke*
hal.structure.identifier
dc.contributor.authorDel Moral, Pierre
HAL ID: 740764
*
hal.structure.identifier
dc.contributor.authorDoucet, Arnaud*
hal.structure.identifier
dc.contributor.authorJacob, Pierre E.
HAL ID: 16651
ORCID: 0000-0002-4662-1051
*
dc.date.accessioned2013-10-24T08:22:13Z
dc.date.available2013-10-24T08:22:13Z
dc.date.issued2013
dc.identifier.issn1061-8600
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11916
dc.language.isoenen
dc.subjectWang-Landau algorithm
dc.subjectParallel chains
dc.subjectAdaptive Markov chain Monte Carlo
dc.subjectAdaptive binning
dc.subject.ddc519en
dc.subject.classificationjelC15
dc.subject.classificationjelC11
dc.titleAn Adaptive Interacting Wang–Landau Algorithm for Automatic Density Exploration
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherDepartment of Statistics , Oxford University;Royaume-Uni
dc.contributor.editoruniversityotherINRIA Bordeaux Sud-Ouest and University of Bordeaux;France
dc.contributor.editoruniversityotherDepartment of Statistics , Harvard University;États-Unis
dc.description.abstractenWhile statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area that we feel deserves much further attention. Toward this aim, this article proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang–Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains—a feature that both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance of this new parallel adaptive Wang–Landau algorithm is studied in several applications. Through a Bayesian variable selection example, we demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm’s adaptive proposal to induce mode-jumping is illustrated through a Bayesian mixture modeling application. Last, through a two-dimensional Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models. Supplemental materials are available online.
dc.relation.isversionofjnlnameJournal of Computational and Graphical Statistics
dc.relation.isversionofjnlvol22
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages749-773
dc.relation.isversionofdoihttp://dx.doi.org/10.1080/10618600.2012.723569
dc.relation.isversionofjnlpublisherTaylor & Francis
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2018-07-23T11:37:01Z
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