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Approximate Bayesian recursive estimation
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SYSNO ASEP 0425539 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Approximate Bayesian recursive estimation Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID Number of authors 1 Source Title Information Sciences. - : Elsevier - ISSN 0020-0255
Roč. 285, č. 1 (2014), s. 100-111Number of pages 12 s. Publication form Print - P Language eng - English Country US - United States Keywords Approximate parameter estimation ; Bayesian recursive estimation ; Kullback–Leibler divergence ; Forgetting Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA13-13502S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000342540700007 EID SCOPUS 84894058260 DOI 10.1016/j.ins.2014.01.048 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2015
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