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Authors: Šárka Jozová 1 ; 2 ; Evženie Uglickich 1 and Ivan Nagy 1

Affiliations: 1 Department of Signal Processing, The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod vodárenskou věží 4, 18208 Prague, Czech Republic ; 2 Faculty of Transportation Sciences, Czech Technical University, Na Florenci 25, 11000 Prague, Czech Republic

Keyword(s): Data Analysis, Clustering, Classification, Mixture Model, Estimation, Prior Knowledge.

Abstract: 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.

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Paper citation in several formats:
Jozová, Š., Uglickich, E. and Nagy, I. (2021). Bayesian Mixture Estimation without Tears. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 641-648. DOI: 10.5220/0010508706410648

@conference{icinco21,
author={Šárka Jozová and Evženie Uglickich and Ivan Nagy},
title={Bayesian Mixture Estimation without Tears},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2021},
pages={641-648},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010508706410648},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Bayesian Mixture Estimation without Tears
SN - 978-989-758-522-7
IS - 2184-2809
AU - Jozová, Š.
AU - Uglickich, E.
AU - Nagy, I.
PY - 2021
SP - 641
EP - 648
DO - 10.5220/0010508706410648
PB - SciTePress

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