About: Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Marginalized Particle Filtering Framework for Tuning of Ensemble Filters Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Marginalized particle ltering (MPF), also known as Rao-Blackwellized particle filtering has been recently developed as a hybrid method combining analytical lters with particle filters. In this paper, we investigate the prospects of this approach in enviromental modelling where the key concerns are nonlinearity, high-dimensionality, and computational cost. In our formulation, exact marginalization in the MPF is replaced by approximate marginalization yielding a framework for creation of new hybrid lters. In particular, we propose to use the MPF framework for on-line tuning of nuisance parameters of ensemble filters. Strength of the framework is demonstrated on the joint estimation of the inflation factor, the measurement error variance and the length-scale parameter of covariance localization. It is shown that accurate estimation can be achieved with a moderate number of particles. Moreover, this result was achieved with naively chosen proposal densities leaving space for further improvements. Marginalized particle ltering (MPF), also known as Rao-Blackwellized particle filtering has been recently developed as a hybrid method combining analytical lters with particle filters. In this paper, we investigate the prospects of this approach in enviromental modelling where the key concerns are nonlinearity, high-dimensionality, and computational cost. In our formulation, exact marginalization in the MPF is replaced by approximate marginalization yielding a framework for creation of new hybrid lters. In particular, we propose to use the MPF framework for on-line tuning of nuisance parameters of ensemble filters. Strength of the framework is demonstrated on the joint estimation of the inflation factor, the measurement error variance and the length-scale parameter of covariance localization. It is shown that accurate estimation can be achieved with a moderate number of particles. Moreover, this result was achieved with naively chosen proposal densities leaving space for further improvements. (en)
Title	Marginalized Particle Filtering Framework for Tuning of Ensemble Filters Marginalized Particle Filtering Framework for Tuning of Ensemble Filters (en)
skos:prefLabel	Marginalized Particle Filtering Framework for Tuning of Ensemble Filters Marginalized Particle Filtering Framework for Tuning of Ensemble Filters (en)
skos:notation	RIV/67985556:_____/11:00367533!RIV12-MV0-67985556
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(GP102/08/P250), P(VG20102013018), Z(AV0Z10750506)
http://linked.open...iv/cisloPeriodika	11
http://linked.open...vai/riv/dodaniDat	2012
http://linked.open...aciTvurceVysledku	Šmídl, Václav Hofman, Radek
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	210675
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/11:00367533
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	ensemble finter; marginalized particle filter; data assimilation (en)
http://linked.open.../riv/klicoveSlovo	ensemble finter marginalized particle filter data assimilation
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[53B45065F28B]
http://linked.open...i/riv/nazevZdroje	Monthly Weather Review
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Stochastic sequential sampling for identification adn control of distributed systems Application of advanced statistical assimilation techniques of model predictions with observations from terrain in the form of modern software tool for emergency management support
http://linked.open...UplatneniVysledku	2011
http://linked.open...v/svazekPeriodika	139
http://linked.open...iv/tvurceVysledku	Hofman, Radek Šmídl, Václav
http://linked.open...ain/vavai/riv/wos	000296475700014
http://linked.open...n/vavai/riv/zamer	Pokročilé matematické metody získávání, zpracování a využití informací a znalostí ve složitých a nedeterministických systémech
issn	0027-0644
number of pages	10 (xsd:int)
http://bibframe.org/vocab/doi	10.1175/2011MWR3586.1

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software