About: Approximate Bayesian recursive estimation

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. (en)
Title	Approximate Bayesian recursive estimation Approximate Bayesian recursive estimation (en)
skos:prefLabel	Approximate Bayesian recursive estimation Approximate Bayesian recursive estimation (en)
skos:notation	RIV/67985556:_____/14:00425539!RIV15-GA0-67985556
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GA13-13502S)
http://linked.open...iv/cisloPeriodika	1
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Kárný, Miroslav
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	3922
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/14:00425539
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Approximate parameter estimation; Bayesian recursive estimation; Kullback–Leibler divergence; Forgetting (en)
http://linked.open.../riv/klicoveSlovo	Forgetting Kullback–Leibler divergence Approximate parameter estimation Bayesian recursive estimation
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[7B2AE885BAB4]
http://linked.open...i/riv/nazevZdroje	Information Sciences
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...vavai/riv/projekt	Fully Probabilistic Design of Dynamic Decision Strategies for Imperfect Participants in Market Scenarios
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	285
http://linked.open...iv/tvurceVysledku	Kárný, Miroslav
http://linked.open...ain/vavai/riv/wos	000342540700007
issn	0020-0255
number of pages	12 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.ins.2014.01.048

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