Particle Learning for General Mixtures

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dc.contributor.authorCarvalho, Carlos M.
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorPolson, Nicholas G.
dc.contributor.authorTaddy, Matt A.
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorCarvalho, Carlos M.
dc.creatorPolson, Nicholas G.
dc.creatorTaddy, Matt A.
dc.date.accessioned2022-10-05T23:23:15Z
dc.date.available2022-10-05T23:23:15Z
dc.date.issued2010
dc.description.otherThis paper develops particle learning (PL) methods for the estimation of general mixture models. The approach is distinguished from alternative particle filtering methods in two major ways. First, each iteration begins by resampling particles according to posterior predictive probability, leading to a more efficient set for propagation. Second, each particle tracks only the “essential state vector” thus leading to reduced dimensional inference. In addition, we describe how the approach will apply to more general mixture models of current interest in the literature; it is hoped that this will inspire a greater number of researchers to adopt sequential Monte Carlo methods for fitting their sophisticated mixture based models. Finally, we show that PL leads to straightforward tools for marginal likelihood calculation and posterior cluster allocation.pt_BR
dc.format.extentp. 709-740pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1214/10-BA525pt_BR
dc.identifier.issue4pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4143
dc.identifier.volume5pt_BR
dc.language.isoInglêspt_BR
dc.publisherNão informadopt_BR
dc.relation.ispartofBayesian Analysispt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.subject.keywordsNonparametricpt_BR
dc.subject.keywordsmixture modelspt_BR
dc.subject.keywordsparticle filteringpt_BR
dc.subject.keywordsDirichlet processpt_BR
dc.subject.keywordsIndian buffet processpt_BR
dc.subject.keywordsprobit stick-breakingpt_BR
dc.titleParticle Learning for General Mixturespt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://projecteuclid.org/journals/bayesian-analysis/volume-5/issue-4/Particle-learning-for-general-mixtures/10.1214/10-BA525.full
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
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