Combining variational data assimilation and particle filters: the variational mapping particle filter

Autores
Pulido, Manuel Arturo; van Leeuwen, Peter Jan
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Recent works in the machine learning community have started to combine two classical statistical concepts: Monte Carlo sampling and variational inference. In the traditional variational inference, including variational data assimilation, some parameters of a proposed posterior density are estimated through maximazing the marginal likelihood or via maximum a posteriori estimation.
Fil: Pulido, Manuel Arturo. University of Reading; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
Fil: van Leeuwen, Peter Jan. University of Reading; Reino Unido
EGU General Assembly 2019
Vienna
Austria
European Geosciences Union
Materia
STEIN DISCREPANCY
SWARM OPTIMIZATION
SEQUENTIAL MONTE CARLO
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/131221
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Combining variational data assimilation and particle filters: the variational mapping particle filterPulido, Manuel Arturovan Leeuwen, Peter JanSTEIN DISCREPANCYSWARM OPTIMIZATIONSEQUENTIAL MONTE CARLOhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Recent works in the machine learning community have started to combine two classical statistical concepts: Monte Carlo sampling and variational inference. In the traditional variational inference, including variational data assimilation, some parameters of a proposed posterior density are estimated through maximazing the marginal likelihood or via maximum a posteriori estimation.Fil: Pulido, Manuel Arturo. University of Reading; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaFil: van Leeuwen, Peter Jan. University of Reading; Reino UnidoEGU General Assembly 2019ViennaAustriaEuropean Geosciences UnionCopernicus Publications2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/131221Combining variational data assimilation and particle filters: the variational mapping particle filter; EGU General Assembly 2019; Vienna; Austria; 20191607-7962CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://meetingorganizer.copernicus.org/EGU2019/EGU2019-5192.pdfInternacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-03-31T15:16:42Zoai:ri.conicet.gov.ar:11336/131221instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-03-31 15:16:42.769CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Combining variational data assimilation and particle filters: the variational mapping particle filter
title Combining variational data assimilation and particle filters: the variational mapping particle filter
spellingShingle Combining variational data assimilation and particle filters: the variational mapping particle filter
Pulido, Manuel Arturo
STEIN DISCREPANCY
SWARM OPTIMIZATION
SEQUENTIAL MONTE CARLO
title_short Combining variational data assimilation and particle filters: the variational mapping particle filter
title_full Combining variational data assimilation and particle filters: the variational mapping particle filter
title_fullStr Combining variational data assimilation and particle filters: the variational mapping particle filter
title_full_unstemmed Combining variational data assimilation and particle filters: the variational mapping particle filter
title_sort Combining variational data assimilation and particle filters: the variational mapping particle filter
dc.creator.none.fl_str_mv Pulido, Manuel Arturo
van Leeuwen, Peter Jan
author Pulido, Manuel Arturo
author_facet Pulido, Manuel Arturo
van Leeuwen, Peter Jan
author_role author
author2 van Leeuwen, Peter Jan
author2_role author
dc.subject.none.fl_str_mv STEIN DISCREPANCY
SWARM OPTIMIZATION
SEQUENTIAL MONTE CARLO
topic STEIN DISCREPANCY
SWARM OPTIMIZATION
SEQUENTIAL MONTE CARLO
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recent works in the machine learning community have started to combine two classical statistical concepts: Monte Carlo sampling and variational inference. In the traditional variational inference, including variational data assimilation, some parameters of a proposed posterior density are estimated through maximazing the marginal likelihood or via maximum a posteriori estimation.
Fil: Pulido, Manuel Arturo. University of Reading; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
Fil: van Leeuwen, Peter Jan. University of Reading; Reino Unido
EGU General Assembly 2019
Vienna
Austria
European Geosciences Union
description Recent works in the machine learning community have started to combine two classical statistical concepts: Monte Carlo sampling and variational inference. In the traditional variational inference, including variational data assimilation, some parameters of a proposed posterior density are estimated through maximazing the marginal likelihood or via maximum a posteriori estimation.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/131221
Combining variational data assimilation and particle filters: the variational mapping particle filter; EGU General Assembly 2019; Vienna; Austria; 2019
1607-7962
CONICET Digital
CONICET
url http://hdl.handle.net/11336/131221
identifier_str_mv Combining variational data assimilation and particle filters: the variational mapping particle filter; EGU General Assembly 2019; Vienna; Austria; 2019
1607-7962
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://meetingorganizer.copernicus.org/EGU2019/EGU2019-5192.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Copernicus Publications
publisher.none.fl_str_mv Copernicus Publications
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.822162

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