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An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

hal.structure.identifierDepartment of Statistics [Vancouver] [UBC Statistics]
dc.contributor.authorBORNN, Luke
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorJACOB, Pierre E.
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDEL MORAL, Pierre
hal.structure.identifierDepartment of Statistics
dc.contributor.authorDOUCET, Arnaud
dc.date.issued2013
dc.identifier.issn1061-8600
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.language.isoen
dc.publisherTaylor & Francis
dc.title.enAn Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration
dc.typeArticle de revue
dc.identifier.doi10.1080/10618600.2012.723569
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
dc.identifier.arxiv1109.3829
bordeaux.journalJournal of Computational and Graphical Statistics
bordeaux.volume22
bordeaux.issue3
bordeaux.peerReviewedoui
hal.identifierhal-00932238
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00932238v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Computational%20and%20Graphical%20Statistics&rft.date=2013&rft.volume=22&rft.issue=3&rft.eissn=1061-8600&rft.issn=1061-8600&rft.au=BORNN,%20Luke&JACOB,%20Pierre%20E.&DEL%20MORAL,%20Pierre&DOUCET,%20Arnaud&rft.genre=article


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