Bayesian Mixture Estimation without Tears

0544577 - ÚTIA 2022 RIV PT eng C - Conference Paper (international conference)
Jozová, Šárka - Uglickich, Evženie - Nagy, Ivan
Bayesian Mixture Estimation without Tears.
Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics. Setúbal: Scitepress, 2021 - (Gusikhin, O.; Nijmeijer, H.; Madani, K.), s. 641-648. ISBN 978-989-758-522-7. ISSN 2184-2809.
[International Conference on Informatics in Control, Automation and Robotics 2021 /18./. Setúbal (online) (PT), 06.07.2021-08.07.2021]
R&D Projects: GA MŠMT(CZ) 8A19009
Institutional support: RVO:67985556
Keywords : Data Analysis * Clustering * Classification * Mixture Model * Estimation * Prior Knowledge
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2021/ZS/uglickich-0544577.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0321817