This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n18http://localhost/temp/predkladatel/
n20http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n8http://linked.opendata.cz/resource/domain/vavai/projekt/
n10http://linked.opendata.cz/resource/domain/vavai/subjekt/
n9http://linked.opendata.cz/ontology/domain/vavai/
n12http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n21http://bibframe.org/vocab/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n6http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n17http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n4http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n19http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n15http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21260%2F12%3A00187538%21RIV13-MSM-21260___/
n11http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21260%2F12%3A00187538%21RIV13-MSM-21260___
rdf:type
skos:Concept n9:Vysledek
dcterms:description
The authors are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. They propose a linear regression model within Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixture, where the weights of components are tuned with a particle filter. The mixture reflects apriori given hypotheses on different scenarios of (expected) parameters' evolution. The resulting hybrid filter locally optimizes the weights to achieve the best fit of a nonlinear signal with a single linear model. The authors are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. They propose a linear regression model within Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixture, where the weights of components are tuned with a particle filter. The mixture reflects apriori given hypotheses on different scenarios of (expected) parameters' evolution. The resulting hybrid filter locally optimizes the weights to achieve the best fit of a nonlinear signal with a single linear model.
dcterms:title
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
skos:prefLabel
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
skos:notation
RIV/68407700:21260/12:00187538!RIV13-MSM-21260___
n9:predkladatel
n10:orjk%3A21260
n3:aktivita
n17:Z n17:S n17:P
n3:aktivity
P(GA102/08/0567), P(VG20102013018), S, Z(AV0Z10750506)
n3:cisloPeriodika
5
n3:dodaniDat
n11:2013
n3:domaciTvurceVysledku
n20:2692139
n3:druhVysledku
n19:J
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
124269
n3:idVysledku
RIV/68407700:21260/12:00187538
n3:jazykVysledku
n4:eng
n3:klicovaSlova
Bayesian methods; particle filters; recursive estimation
n3:klicoveSlovo
n6:Bayesian%20methods n6:recursive%20estimation n6:particle%20filters
n3:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n3:kontrolniKodProRIV
[23D2B34F764D]
n3:nazevZdroje
Communications in Statistics - Simulation and computation
n3:obor
n14:BB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n8:GA102%2F08%2F0567 n8:VG20102013018
n3:rokUplatneniVysledku
n11:2012
n3:svazekPeriodika
41
n3:tvurceVysledku
Dedecius, Kamil Hofman, R.
n3:wos
000301342800002
n3:zamer
n12:AV0Z10750506
s:issn
0361-0918
s:numberOfPages
8
n21:doi
10.1080/03610918.2011.598992
n18:organizacniJednotka
21260