0557467 20240111141105.520220518235959.9 Prague UTIA AV ČR, v. v. i., 2022 14 s. P Research Report 2394 recursive mixture estimation mixture of Poisson distributions clustering and classification 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/2022/ZS/uglickich-0557467.pdf 8A19009 GA MŠk CZ cav_un_auth*0385121 Analysis of count variables described by the Poisson distribution is required in many application fields. Examples of the count variables observed per a time unit can be, e.g., number of customers, passengers, road accidents, Internet traffic packet arrivals, bankruptcies, virus attacks, etc. If the behavior of such a variable exhibits a multimodal character, the problem of clustering and classification of incoming count data arises. This issue can touch, for instance, detecting clusters of the different behavior of drivers in traffic flow analysis as well as cyclists or pedestrians. This work focuses on the model-based clustering of Poisson-distributed count data with the help of the recursive Bayesian estimation of the mixture of Poisson components. The aim of the work is to explain the methodology in details with an illustrative simple example, so that the work is limited to the univariate case and static pointer. BB 10000 10100 10103 2023 2 4 O 4o 20231122150545.8 http://hdl.handle.net/11104/0331506 S 2022 Soubory v repozitáři: 0557467.pdf 2022 Prague UTIA AV ČR, v.v.i., pdf