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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F09%3A00315684%21RIV09-GA0-67985556
rdf:type
skos:Concept n16:Vysledek
dcterms:description
Non-symmetric Kullback–Leibler divergence (KLD) measures proximity of probability density functions (pdfs). Bernardo (Ann. Stat. 1979; 7(3):686–690) had shown its unique role in approximation of pdfs. The order of the KLD arguments is also implied by his methodological result. Functional approximation of estimation and stabilized forgetting, serving for tracking of slowly varying parameters, use the reversed order. This choice has the pragmatic motivation: recursive estimator often approximates the parametric model by a member of exponential family (EF) as it maps prior pdfs from the set of conjugate pdfs (CEF) back to the CEF. Approximations based on the KLD with the reversed order of arguments preserves this property. In the paper, the approximation performed within the CEF but with the proper order of arguments of the KLD is advocated. It is applied to the parameter tracking and performance improvements are demonstrated. Non-symmetric Kullback–Leibler divergence (KLD) measures proximity of probability density functions (pdfs). Bernardo (Ann. Stat. 1979; 7(3):686–690) had shown its unique role in approximation of pdfs. The order of the KLD arguments is also implied by his methodological result. Functional approximation of estimation and stabilized forgetting, serving for tracking of slowly varying parameters, use the reversed order. This choice has the pragmatic motivation: recursive estimator often approximates the parametric model by a member of exponential family (EF) as it maps prior pdfs from the set of conjugate pdfs (CEF) back to the CEF. Approximations based on the KLD with the reversed order of arguments preserves this property. In the paper, the approximation performed within the CEF but with the proper order of arguments of the KLD is advocated. It is applied to the parameter tracking and performance improvements are demonstrated. Nesymetrická Kullback-Leiblerova divergence (KLD) měří blízkost pravděpodobnostních hustot. Dá se ukázat, že jedna z jejich verzí je teoreticky lepší. Článek popisuje využití této skutečnosti ke zlepšení techniky zapomínání.
dcterms:title
Use of Kullback–Leibler divergence for forgetting Use of Kullback–Leibler divergence for forgetting Použití Kullback–Leibler divergence pro zapomínání
skos:prefLabel
Use of Kullback–Leibler divergence for forgetting Use of Kullback–Leibler divergence for forgetting Použití Kullback–Leibler divergence pro zapomínání
skos:notation
RIV/67985556:_____/09:00315684!RIV09-GA0-67985556
n3:aktivita
n12:Z n12:P
n3:aktivity
P(1M0572), P(2C06001), P(GA102/08/0567), Z(AV0Z10750506)
n3:cisloPeriodika
1
n3:dodaniDat
n17:2009
n3:domaciTvurceVysledku
n5:6585256 n5:8348553
n3:druhVysledku
n13:J
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
347892
n3:idVysledku
RIV/67985556:_____/09:00315684
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Bayesian estimation; Kullback–Leibler divergence; functional approximation of estimation; parameter tracking by stabilized forgetting; ARX model
n3:klicoveSlovo
n10:ARX%20model n10:parameter%20tracking%20by%20stabilized%20forgetting n10:functional%20approximation%20of%20estimation n10:Kullback%E2%80%93Leibler%20divergence n10:Bayesian%20estimation
n3:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n3:kontrolniKodProRIV
[8DD570FEA618]
n3:nazevZdroje
International Journal of Adaptive Control and Signal Processing
n3:obor
n18:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n9:1M0572 n9:2C06001 n9:GA102%2F08%2F0567
n3:rokUplatneniVysledku
n17:2009
n3:svazekPeriodika
23
n3:tvurceVysledku
Andrýsek, Josef Kárný, Miroslav
n3:zamer
n11:AV0Z10750506
s:issn
0890-6327
s:numberOfPages
15