Original title: Approximating Probability Densities by Mixtures of Gaussian Dependence Trees
Authors: Grim, Jiří
Document type: Papers
Conference/Event: Stochastic and Physical Monitoring Systems SPMS 2014, Malá Skála (CZ), 2014-06-23 / 2014-06-28
Year: 2014
Language: eng
Abstract: Considering the probabilistic approach to practical problems we are increasingly confronted with the need to estimate unknown multivariate probability density functions from large high-dimensional databases produced by electronic devices. The underlying densities are usually strongly multimodal and therefore mixtures of unimodal density functions suggest themselves as a suitable approximation tool. In this respect the product mixture models are preferable because they can be efficiently estimated from data by means of EM algorithm and have some advantageous properties. However, in some cases the simplicity of product components could appear too restrictive and a natural idea is to use a more complex mixture of dependence-tree densities. The dependence tree densities can explicitly describe the statistical relationships between pairs of variables at the level of individual components and therefore the approximation power of the resulting mixture may essentially increase.
Keywords: EM algorithm; Medical image analysis; Mixtures of dependence trees; Multivariate statistics; Pattern recognition
Project no.: GA14-02652S (CEP), GA14-10911S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Stochastic and Physical Monitoring Systems, SPMS 2014, ISBN 978-80-01-05616-5

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2014/RO/grim-0435901.pdf
Original record: http://hdl.handle.net/11104/0241872

Permalink: http://www.nusl.cz/ntk/nusl-180371


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