0544577 20240111141054.520210810235959.9 10.5220/0010508706410648 8 s. E cav_un_epca*0543770978-989-758-522-72184-2809641-648SetúbalScitepress2021GusikhinO.NijmeijerH.MadaniK. Data Analysis Clustering Classification Mixture Model Estimation Prior Knowledge cav_un_auth*0412278 Jozová Šárka UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0383037 Uglickich Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2021/ZS/uglickich-0544577.pdf 8A19009 GA MŠk CZ cav_un_auth*0385121 This paper aims at presenting the on-line non-iterative form of Bayesian mixture estimation. The model used is composed of a set of sub-models (components) and an estimated pointer variable that currently indicates the active component. The estimation is built on an approximated Bayes rule using weighted measured data. The weights are derived from the so called proximity of measured data entries to individual components. The basis for the generation of the weights are integrated likelihood functions with the inserted point estimates of the component parameters. One of the main advantages of the presented data analysis method is a possibility of a simple incorporation of the available prior knowledge. Simple examples with a programming code as well as results of experiments with real data are demonstrated. The main goal of this paper is to provide clear description of the Bayesian estimation method based on the approximated likelihood functions, called proximities. cav_un_auth*0411344 International Conference on Informatics in Control, Automation and Robotics 2021 /18./ 20210706 20210708 Setúbal (online) PT BB 10000 10100 10103 2022 3 PR RVO:67985556 http://hdl.handle.net/11104/0321817 S 5 16.67 2021 SCOPUS PUBMED WOS pdf cav_un_epca*0543770 Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics Scitepress 2021 Setúbal 641 648 978-989-758-522-7 2184-2809 Gusikhin O. 340 Nijmeijer H. 340 Madani K. 340