Online Bayesian learning in dynamic models: An illustrative introduction to particle methods

dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorCarvalho, Carlos M.
dc.coverage.paisEstados Unidospt_BR
dc.creatorCarvalho, Carlos M.
dc.date.accessioned2022-12-15T19:56:59Z
dc.date.available2022-12-15T19:56:59Z
dc.date.issued2013
dc.description.otherThis chapter reviews the main advances, over the last two decades, in the particle filter (PF) literature for dynamic models. We focus the discussion around the bootstrap filter (BF) and the auxiliary particle filter (APF), as these are the basis for most of the contributions in the literature. Both filters are then extended to accommodate sequential parameter learning, an area that has gained renewed attention over the last couple of years. The chapter is mainly intended for those researchers and practitioners with little or no practical experience with PF and are looking for a hands-on approach to the subject. With that in mind, we implement and compare the discussed particle filters in two well known contexts: the AR(1) plus noise model and the stochastic volatility model with AR(1) dynamics, or simply SV-AR(1) model. The AR(1) plus noise model is used as a benchmark since all sequential distributions are available in closed-form when parameters are kept fixed. The SV-AR(1) provides an illustration of the ability of PF to deal with traditionally challenging non-linear models.pt_BR
dc.format.extentp. 203-228pt_BR
dc.format.mediumFísicopt_BR
dc.identifier.isbn198739079pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4975
dc.language.isoInglêspt_BR
dc.publisherOxford University Presspt_BR
dc.relation.isreferencedbyBayesian Theory and Applicationspt_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.titleOnline Bayesian learning in dynamic models: An illustrative introduction to particle methodspt_BR
dc.typebook part
dspace.entity.typePublication
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeCapítulo de Livropt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
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