0525233 20240103224203.620200623235959.9 85090153735 000613138000007 10.23919/ECC51009.2020.9143856 6 s. C cav_un_epca*0524379978-390714401-535-40Saint PetersburgEuropean Union Control Association (EUCA)2020 minimum relative entropy principle uncertain prior probability forgetting fully probabilistic design abrupt parameter changes cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems K Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2020/AS/karny-0525233.pdf LTC18075 GA MŠk CZ cav_un_auth*0372050 CA16228 The European Cooperation in Science and Technology (COST) XE cav_un_auth*0372051 Stochastic filtering estimates a timevarying (multivariate) parameter (a hidden variable) from noisy observations. It needs both observation and parameter evolution models. The latter is often missing or makes the estimation too complex. Then, the axiomatic minimum relative entropy (MRE) principle completes the posterior probability density (pd) of the parameter. The MRE principle recommends to modify a prior guess of the constructed pd to the smallest extent enforced by new observations. The MRE principle does not deal with a generic uncertain prior guess. Such uncertainty arises, for instance, when the MRE principle is used recursively. The paper fills this gap. The proposed minimum expected relative entropy (MeRE) principle: (a) makes Bayesian estimation less sensitive to the choice of the prior pd. (b) provides a stabilised parameter tracking with a data-dependent forgetting that copes with abrupt parameter changes. (c) applies in all cases exploiting MRE, for instance, in stochastic modelling. cav_un_auth*0392145 The European Control Conference (ECC 2020) 20200512 20200515 Saint Petersburg RU BC 10000 10200 10201 2021 4 A sml 4as 20231122145000.9 PR RVO:67985556 http://hdl.handle.net/11104/0309415 S Transfer of Copyright Agreement 20200214 n.a. Proceedings Paper Automation Control Systems|Engineering Electrical Electronic|Operations Research Management Science|Mathematics Applied 2020 Soubory v repozitáři: karny-0525233-CopyrightForm_426.pdf 85090153735 SCOPUS PUBMED 000613138000007 WOS cav_un_epca*0524379 Proceedings of the 18th European Control Conference (ECC) 978-390714401-5 35 40 Saint Petersburg European Union Control Association (EUCA) 2020