Bayesian solutions to the label switching problem

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorPuolamäki, Kai
dc.contributor.authorKaski, Samuel
dc.contributor.departmentDepartment of Information and Computer Scienceen
dc.contributor.departmentTietojenkäsittelytieteen laitosfi
dc.contributor.schoolFaculty of Information and Natural Sciencesen
dc.contributor.schoolInformaatio- ja luonnontieteiden tiedekuntafi
dc.date.accessioned2011-11-28T13:18:32Z
dc.date.available2011-11-28T13:18:32Z
dc.date.issued2008
dc.description.abstractThe label switching problem, the unidentifiability of the permutation of clusters or more generally latent variables, makes interpretation of results computed with MCMC sampling difficult. We introduce a fully Bayesian treatment of the permutations which performs better than alternatives. The method can be used to compute summaries of the posterior samples even for nonparametric Bayesian methods, for which no good solutions exist so far. Although being approximative in this case, the results are very promising. The summaries are intuitively appealing: A summarized cluster is defined as a set of points for which the likelihood of being in the same cluster is maximized.en
dc.format.extentiii, 8
dc.format.mimetypeapplication/pdf
dc.identifier.isbn978-951-22-9493-0
dc.identifier.issn1797-5042
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/877
dc.identifier.urnurn:nbn:fi:tkk-011496
dc.language.isoenen
dc.publisherHelsinki University of Technologyen
dc.publisherTeknillinen korkeakoulufi
dc.relation.ispartofseriesTKK reports in information and computer scienceen
dc.relation.ispartofseries7en
dc.subject.keywordlabel switchingen
dc.subject.keywordmixture modelsen
dc.subject.otherComputer scienceen
dc.subject.otherMathematicsen
dc.titleBayesian solutions to the label switching problemen
dc.typeD4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitysfi
dc.type.dcmitypetexten
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