Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data

0544576 20240111141054.520210810235959.9 10.5220/0010575006000608 Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data 9 s. E cav_un_epca*0543770978-989-758-522-72184-2809Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics600-608SetúbalScitepress2021GusikhinO.NijmeijerH.MadaniK. Poisson Distribution Prediction Discrete Data Discretization Mixture based Clustering Bayesian Recursive Mixture Estimation 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. 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. pdf http://library.utia.cas.cz/separaty/2021/ZS/uglickich-0544576.pdf 8A19009 GA MŠk CZ cav_un_auth*0385121 The paper deals with predicting a discrete target variable described by the Poisson distribution based on the discretized Gaussian explanatory data under condition of the multimodality of a system observed. The discretization is performed using the recursive mixture-based clustering algorithms under Bayesian methodology. The proposed approach allows to estimate the Gaussian and Poisson models existing for each discretization interval of explanatory data and use them for the prediction. The main contributions of the approach include: (i) modeling the Poisson variable based on the cluster analysis of explanatory continuous data, (ii) the discretization approach based on recursive mixture estimation theory, (iii) the online prediction of the Poisson variable based on available Gaussian data discretized in real time. Results of illustrative experiments and comparison with the Poisson regression is demonstrated. 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/11104/0321816 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 600 608 978-989-758-522-7 2184-2809 Gusikhin O. 340 Nijmeijer H. 340 Madani K. 340