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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F14%3A00425539%21RIV15-GA0-67985556
rdf:type
skos:Concept n12:Vysledek
dcterms:description
Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc.). Primarily, it relies on a recursive parameter estimation of a firmly bounded complexity. As a rule, it has to approximate the exact posterior probability density (pd), which comprises unreduced information about the estimated parameter. In the recursive treatment of the data stream, the latest approximate pd is usually updated using the treated parametric model and the newest data and then approximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects the estimator design with respect to the error accumulation and concludes that a sort of forgetting (pd flattening) is an indispensable part of a reliable approximate recursive estimation. Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc.). Primarily, it relies on a recursive parameter estimation of a firmly bounded complexity. As a rule, it has to approximate the exact posterior probability density (pd), which comprises unreduced information about the estimated parameter. In the recursive treatment of the data stream, the latest approximate pd is usually updated using the treated parametric model and the newest data and then approximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects the estimator design with respect to the error accumulation and concludes that a sort of forgetting (pd flattening) is an indispensable part of a reliable approximate recursive estimation.
dcterms:title
Approximate Bayesian recursive estimation Approximate Bayesian recursive estimation
skos:prefLabel
Approximate Bayesian recursive estimation Approximate Bayesian recursive estimation
skos:notation
RIV/67985556:_____/14:00425539!RIV15-GA0-67985556
n3:aktivita
n13:P n13:I
n3:aktivity
I, P(GA13-13502S)
n3:cisloPeriodika
1
n3:dodaniDat
n4:2015
n3:domaciTvurceVysledku
n10:6585256
n3:druhVysledku
n11:J
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
3922
n3:idVysledku
RIV/67985556:_____/14:00425539
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Approximate parameter estimation; Bayesian recursive estimation; Kullback–Leibler divergence; Forgetting
n3:klicoveSlovo
n5:Bayesian%20recursive%20estimation n5:Forgetting n5:Approximate%20parameter%20estimation n5:Kullback%E2%80%93Leibler%20divergence
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[7B2AE885BAB4]
n3:nazevZdroje
Information Sciences
n3:obor
n16:BB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n18:GA13-13502S
n3:rokUplatneniVysledku
n4:2014
n3:svazekPeriodika
285
n3:tvurceVysledku
Kárný, Miroslav
n3:wos
000342540700007
s:issn
0020-0255
s:numberOfPages
12
n7:doi
10.1016/j.ins.2014.01.048