Particle Learning and Smoothing
| dc.contributor.author | Carvalho, Carlos M. | |
| dc.contributor.author | Michael S. Johannes | |
| dc.contributor.author | HEDIBERT FREITAS LOPES | |
| dc.contributor.author | Polson, Nicholas G. | |
| dc.coverage.pais | Não Informado | pt_BR |
| dc.creator | Carvalho, Carlos M. | |
| dc.creator | Michael S. Johannes | |
| dc.creator | Polson, Nicholas G. | |
| dc.date.accessioned | 2022-10-05T23:15:59Z | |
| dc.date.available | 2022-10-05T23:15:59Z | |
| dc.date.issued | 2010 | |
| dc.description.other | Particle 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.extent | p. 88-106 | pt_BR |
| dc.format.medium | Digital | pt_BR |
| dc.identifier.doi | 10.1214/10-STS325 | pt_BR |
| dc.identifier.issue | 1 | pt_BR |
| dc.identifier.uri | https://repositorio.insper.edu.br/handle/11224/4142 | |
| dc.identifier.volume | 25 | pt_BR |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | Não informado | pt_BR |
| dc.relation.ispartof | Statistical Science | pt_BR |
| dc.rights.license | O 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.keywords | Mixture Kalman filter | pt_BR |
| dc.subject.keywords | parameter learning | pt_BR |
| dc.subject.keywords | particle learning | pt_BR |
| dc.subject.keywords | sequential inference | pt_BR |
| dc.subject.keywords | smoothing | pt_BR |
| dc.subject.keywords | state filtering | pt_BR |
| dc.subject.keywords | state space models | pt_BR |
| dc.title | Particle Learning and Smoothing | pt_BR |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| local.identifier.sourceUri | https://projecteuclid.org/journals/statistical-science/volume-25/issue-1/Particle-Learning-and-Smoothing/10.1214/10-STS325.full | |
| local.subject.cnpq | Ciências Sociais Aplicadas | pt_BR |
| local.type | Artigo Científico | pt_BR |
| relation.isAuthorOfPublication | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | |
| relation.isAuthorOfPublication.latestForDiscovery | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca |
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