Particle Learning and Smoothing

dc.contributor.authorCarvalho, Carlos M.
dc.contributor.authorMichael S. Johannes
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorPolson, Nicholas G.
dc.coverage.paisNão Informadopt_BR
dc.creatorCarvalho, Carlos M.
dc.creatorMichael S. Johannes
dc.creatorPolson, Nicholas G.
dc.date.accessioned2022-10-05T23:15:59Z
dc.date.available2022-10-05T23:15:59Z
dc.date.issued2010
dc.description.otherParticle learning (PL) provides state filtering, sequential pa rameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as parti cles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.pt_BR
dc.format.extentp. 88-106pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1214/10-STS325pt_BR
dc.identifier.issue1pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4142
dc.identifier.volume25pt_BR
dc.language.isoInglêspt_BR
dc.publisherNão informadopt_BR
dc.relation.ispartofStatistical Sciencept_BR
dc.relation.urihttps://projecteuclid.org/journals/statistical-science/volume-25/issue-1/Particle-Learning-and-Smoothing/10.1214/10-STS325.fullpt_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 EDITORpt_BR
dc.subject.keywordsMixture Kalman filterpt_BR
dc.subject.keywordsparameter learningpt_BR
dc.subject.keywordsparticle learningpt_BR
dc.subject.keywordssequential inferencept_BR
dc.subject.keywordssmoothingpt_BR
dc.subject.keywordsstate filteringpt_BR
dc.subject.keywordsstate space modelspt_BR
dc.titleParticle Learning and Smoothingpt_BR
dc.typejournal article
dspace.entity.typePublication
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 - 1 de 1
N/D
Nome:
R_2010_Artigo_Particle learning and smoothing_TC.pdf
Tamanho:
1.51 MB
Formato:
Adobe Portable Document Format
Descrição:
R_2010_Artigo_Particle learning and smoothing_TC
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: