This HTML5 document contains 44 embedded RDF statements represented using HTML+Microdata notation.

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

Namespace Prefixes

PrefixIRI
n8http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n20http://purl.org/net/nknouf/ns/bibtex#
n16http://linked.opendata.cz/resource/domain/vavai/projekt/
n15http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n10http://linked.opendata.cz/resource/domain/vavai/subjekt/
n9http://linked.opendata.cz/ontology/domain/vavai/
n22https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
n21http://bibframe.org/vocab/
n19http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F67985556%3A_____%2F12%3A00378658%21RIV13-TA0-67985556/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n13http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n17http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n7http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F12%3A00378658%21RIV13-TA0-67985556
rdf:type
skos:Concept n9:Vysledek
dcterms:description
Recursive estimation forms core of adaptive prediction and control. Dynamic exponential family is the only but narrow class of parametric models that allows exact Bayesian estimation. The paper provides an approximate estimation of important autoregressive model with exogenous variables (ARX) and uniform noise. This model reflects well physical nature of modelled system: majority of signals, noise and estimated parameters are bounded. Unlike former solutions, the paper proposes an algorithm that provides a full (approximate) posterior probability density function (pdf) of unknown parameters. Behaviour of the designed algorithm is illustrated by simulations. Recursive estimation forms core of adaptive prediction and control. Dynamic exponential family is the only but narrow class of parametric models that allows exact Bayesian estimation. The paper provides an approximate estimation of important autoregressive model with exogenous variables (ARX) and uniform noise. This model reflects well physical nature of modelled system: majority of signals, noise and estimated parameters are bounded. Unlike former solutions, the paper proposes an algorithm that provides a full (approximate) posterior probability density function (pdf) of unknown parameters. Behaviour of the designed algorithm is illustrated by simulations.
dcterms:title
Approximate Bayesian Recursive Estimation of Linear Model with Uniform Noise Approximate Bayesian Recursive Estimation of Linear Model with Uniform Noise
skos:prefLabel
Approximate Bayesian Recursive Estimation of Linear Model with Uniform Noise Approximate Bayesian Recursive Estimation of Linear Model with Uniform Noise
skos:notation
RIV/67985556:_____/12:00378658!RIV13-TA0-67985556
n9:predkladatel
n10:ico%3A67985556
n4:aktivita
n17:I n17:P
n4:aktivity
I, P(TA01030123)
n4:dodaniDat
n7:2013
n4:domaciTvurceVysledku
n15:2617137 n15:6585256
n4:druhVysledku
n14:D
n4:duvernostUdaju
n6:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
123478
n4:idVysledku
RIV/67985556:_____/12:00378658
n4:jazykVysledku
n12:eng
n4:klicovaSlova
recursive parameter estimation; bounded noise; Bayesian learning; autoregressive models
n4:klicoveSlovo
n13:autoregressive%20models n13:bounded%20noise n13:recursive%20parameter%20estimation n13:Bayesian%20learning
n4:kontrolniKodProRIV
[83B8A854224D]
n4:mistoKonaniAkce
Brussels
n4:mistoVydani
Brussels
n4:nazevZdroje
Proceedings of the 16th IFAC Symposium on System Identification
n4:obor
n18:BC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n16:TA01030123
n4:rokUplatneniVysledku
n7:2012
n4:tvurceVysledku
Kárný, Miroslav Pavelková, Lenka
n4:typAkce
n8:WRD
n4:zahajeniAkce
2012-07-11+02:00
s:numberOfPages
5
n21:doi
10.3182/20120711-3-BE-2027.00104
n20:hasPublisher
IFAC
n22:isbn
978-3-902823-06-9