Bayesian estimation of mixtures with dynamic transitions and known component parameters

0364115 - ÚTIA 2012 RIV CZ eng J - Journal Article
Nagy, I. - Suzdaleva, Evgenia - Kárný, Miroslav
Bayesian estimation of mixtures with dynamic transitions and known component parameters.
Kybernetika. Roč. 47, č. 4 (2011), s. 572-594. ISSN 0023-5954
R&D Projects: GA MŠMT 1M0572; GA TA ČR TA01030123; GA ČR GA102/08/0567
Grant - others:Skoda Auto(CZ) ENS/2009/UTIA
Institutional research plan: CEZ:AV0Z10750506
Keywords : mixture model * Bayesian estimation * approximation * clustering * classification
Subject RIV: BC - Control Systems Theory
Impact factor: 0.454, year: 2011
http://library.utia.cas.cz/separaty/2011/AS/nagy-bayesian estimation of mixtures with dynamic transitions and known component parameters.pdf

Probabilistic mixtures provide flexible "universal" approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs "naturally" in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant component weights. Thus, mixtures with dynamic components and dynamic transitions between them are not supported. The present paper fills this gap. For the sake of simplicity and to give a better insight into the task, the paper considers mixtures with known components. A general case with unknown components will be presented soon.
Permanent Link: http://hdl.handle.net/11104/0199682