0524580 20240103224108.120200528235959.9 85083664054 10.1109/ICCAIRO47923.2019.00023 92-99 The Institute of Electrical and Electronics Engineers, Inc. 2020 8 s. P cav_un_epca*0524582978-1-7281-3573-192-99PiscatawayIEEE2020 dynamic systems mixture models Bayesian learning mixture ratio cav_un_auth*0333672 Ruman Marko UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS SK Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2020/AS/karny-0524580.pdf LTC18075 GA MŠk CZ cav_un_auth*0372050 CA16228 The European Cooperation in Science and Technology (COST) XE cav_un_auth*0372051 Finite mixtures of probability densities with components from exponential family serve as flexible parametric models of high-dimensional systems. However, with a few specialized exceptions, these dynamic models assume data-independent weights of mixture components. Their use is illogical and restricts the modeling applicability. The requirement for closeness with respect to conditioning, the basic learning operation, leads to a novel class of models: the mixture ratios. The paper justified them and shows their ability to model truly dynamic systems. cav_un_auth*0392578 International Conference on Control, Artificial Intelligence, Robotics & Optimization ICCAIRO 2019 20191208 20191210 Athens GR BD 10000 10200 10201 2021 2 4 A sml 4as 20231122144930.9 PR RVO:67985556 http://hdl.handle.net/11104/0308930 S Copyright 20200130 19510399 2020 Soubory v repozitáři: karny-0524580-CopyrightReceipt.pdf C 2020 The Institute of Electrical and Electronics Engineers, Inc. 85083664054 SCOPUS PUBMED WOS cav_un_epca*0524582 Proceedings of the 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) 978-1-7281-3573-1 92 99 Piscataway IEEE 2020