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Title: Bayesian estimation of mixtures with dynamic transitions and known component parameters (English)
Author: Nagy, Ivan
Author: Suzdaleva, Evgenia
Author: Kárný, Miroslav
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 47
Issue: 4
Year: 2011
Pages: 572-594
Summary lang: English
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Category: math
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Summary: 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. (English)
Keyword: mixture model
Keyword: Bayesian estimation
Keyword: approximation
Keyword: clustering
Keyword: classification
MSC: 68T05
MSC: 93E12
idZBL: Zbl 1227.93114
idMR: MR2884862
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Date available: 2011-09-23T11:25:13Z
Last updated: 2013-09-22
Stable URL: http://hdl.handle.net/10338.dmlcz/141660
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