0425539 20240103203939.720140226235959.9 000342540700007 84894058260 10.1016/j.ins.2014.01.048 12 s. P cav_un_epca*02567520020-02552851 (2014)100-111Elsevier Approximate parameter estimation Bayesian recursive estimation Kullback–Leibler divergence Forgetting cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems 100 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/AS/karny-0425539.pdf GA13-13502S GA ČR cav_un_auth*0292725 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. 2015 BB 1 4 A 4a 20231122140119.7 RVO:67985556 http://hdl.handle.net/11104/0231504 S COMPUTERSCIENCEINFORMATIONSYSTEMS 0.873 2.226 Q1 74.327 96.043 Q2 Q1* 2014 Soubory v repozitáři: karny-0425539.pdf 84894058260 SCOPUS 000342540700007 WOS cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 285 č. 1 2014 100 111 Elsevier