0524975 20240111141038.420200615235959.9 85087422156 000610510000012 10.1109/SACI49304.2020.9118836 6 s. E cav_un_epca*0525235978-1-7281-7378-877-82PiscatawayIEEE2020 mixed data Poisson distribution mixture based clustering passenger demand cav_un_auth*0349960 Petrouš Matej 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. pdf http://library.utia.cas.cz/separaty/2020/AS/uglickich-0524975.pdf 8A17006 GA MŠk CZ cav_un_auth*0351997 The paper deals with the task of modeling mixed continuous Gaussian and discrete Poisson data observed on a multimodal system. The proposed solution is based on recursive algorithms of Bayesian mixture estimation. The main contributions of the approach are: (i) the use of the discretized information of normal variables in the form of their clusters in order to keep the one-pass recursive estimation methodology and (ii) the prediction of the multimodal Poisson variable. Experiments with simulated and real data are presented. cav_un_auth*0393003 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics SACI 2020 20200521 20200523 Timisoara RO BB 10000 10100 10103 2021 2 PR RVO:67985556 http://hdl.handle.net/11104/0309417 S n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Theory Methods 2020 85087422156 SCOPUS PUBMED 000610510000012 WOS pdf cav_un_epca*0525235 Applied Computational Intelligence and Informatics (SACI) : 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI) 978-1-7281-7378-8 77 82 Piscataway IEEE 2020