Particle Learning and Smoothing
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...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2010 |
| País: | Brasil |
| Recursos: | Instituição de Ensino Superior e de Pesquisa (INSPER) |
| Repositório: | Repositório Institucional da INSPER |
| Idioma: | inglês |
| OAI Identifier: | oai:repositorio.insper.edu.br:11224/4142 |
| Acesso em linha: | https://repositorio.insper.edu.br/handle/11224/4142 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Mixture Kalman filter parameter learning particle learning sequential inference smoothing state filtering state space models |
| Resumo: | 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. |
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