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hal.structure.identifierDepartment of Statistics [Vancouver] [UBC Statistics]
dc.contributor.authorBORNN, Luke
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCARON, François
dc.date.created2010-05-27
dc.date.issued2011
dc.identifier.issn1936-0975
dc.description.abstractEnIn this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis is placed on a particular prior distribution which derives its motivation from the class of product partition models; the properties of this prior relative to existing priors is examined through theory and simulation. We then demonstrate the use of graphical models in the field of agriculture, showing how the proposed prior distribution alleviates the inflexibility of previous approaches in properly modeling the interactions between the yield of different crop varieties.
dc.language.isoen
dc.publisherInternational Society for Bayesian Analysis
dc.title.enBayesian clustering in decomposable graphs
dc.typeArticle de revue
dc.identifier.doi10.1214/11-BA630
dc.subject.halStatistiques [stat]/Méthodologie [stat.ME]
dc.subject.halStatistiques [stat]/Applications [stat.AP]
dc.subject.halStatistiques [stat]/Autres [stat.ML]
dc.identifier.arxiv1005.5081
bordeaux.journalBayesian Analysis
bordeaux.page829-845
bordeaux.volume6
bordeaux.issue4
bordeaux.peerReviewedoui
bordeaux.type.reportrr
hal.identifierinria-00487168
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00487168v1
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