0372388 20240103200401.720120207235959.9 Praha ÚTIA AV ČR 2012 11 s. Research Report 2317 approximate estimation adaptive systems 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 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/AS/karny-approximate bayesian recursive estimation on approximation errors.pdf 1M0572 GA MŠk cav_un_auth*0001814 GA102/08/0567 GA ČR cav_un_auth*0239566 CEZ:AV0Z10750506 Adaptive systems rely on recursive estimation of a firmly bounded complex- ity. As a rule, they have to use an approximation of the posterior proba- bility density function (pdf), which comprises unreduced information about the estimated parameter. In recursive setting, the latest approximate pdf is updated using the learnt system model and the newest data and then ap- proximated. 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 this problem. 2012 BD 2 4 O 4o 20231122134921.8 http://hdl.handle.net/11104/0205719 2012 Soubory v repozitáři: 0372388.pdf 2012 Praha ÚTIA AV ČR