Particle Learning for Sequential Bayesian Computation

Página do item simplificado
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorCarvalho, Carlos M.
dc.contributor.authorJohannes, Michael S.
dc.contributor.authorPolson, Nicholas G.
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorCarvalho, Carlos M.
dc.creatorJohannes, Michael S.
dc.creatorPolson, Nicholas G.
dc.date.accessioned2022-08-23T20:18:03Z
dc.date.available2022-08-23T20:18:03Z
dc.date.issued2011
dc.description.otherParticle learning provides a simulation-based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive and propagation rule to build a resampling-sampling framework. Predictive inference and sequential Bayes factors are a direct by-product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.pt_BR
dc.format.extentp. 317-360pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4100
dc.identifier.volume9pt_BR
dc.language.isoInglêspt_BR
dc.publisherOxford University Presspt_BR
dc.relation.ispartofBayesian Statisticspt_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.keywordsParticle learningpt_BR
dc.subject.keywordsBayesianpt_BR
dc.subject.keywordsDynamic factor modelspt_BR
dc.subject.keywordsEssential state vectorpt_BR
dc.subject.keywordsMixture modelspt_BR
dc.subject.keywordsSequential inferencept_BR
dc.subject.keywordsConditional dynamic linear modelspt_BR
dc.subject.keywordsNonparametricpt_BR
dc.subject.keywordsDirichletpt_BR
dc.titleParticle Learning for Sequential Bayesian Computationpt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/4772/Particle_Learning.pdf#:~:text=Summary%20Particle%20learning%20provides%20a%20simulation-based%20approach%20to,and%20sequential%20Bayes%20factors%20are%20a%20direct%20by-product.
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
Arquivos
Pacote Original
Agora exibindo 1 - 2 de 2
N/D
Nome:
R_Artigo_2019_Particle Learning for Sequential_TC.pdf
Tamanho:
7.08 MB
Formato:
Adobe Portable Document Format
Descrição:
R_Artigo_2019_Particle Learning for Sequential_TC
Baixar
Imagem de Miniatura
Nome:
Acesso_Primeira Pagina_Particle Learning for Sequential Bayesian Computation.pdf
Tamanho:
83.19 KB
Formato:
Adobe Portable Document Format
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
N/D
Nome:
license.txt
Tamanho:
282 B
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar
Coleções
Coleção de Artigos Acadêmicos