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Approximating Probability Densities by Mixtures of Gaussian Dependence Trees

SYSNO ASEP	0435901
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Approximating Probability Densities by Mixtures of Gaussian Dependence Trees
Author(s)	Grim, Jiří (UTIA-B)_{RID, ORCID}
Number of authors	1
Source Title	Stochastic and Physical Monitoring Systems, SPMS 2014. - Praha : ČVUT, 2014 - ISBN 978-80-01-05616-5
Number of pages	13 s.
Publication form	Print - P
Action	Stochastic and Physical Monitoring Systems SPMS 2014
Event date	23.06.2014-28.06.2014
VEvent location	Malá Skála
Country	CZ - Czech Republic
Event type	EUR
Language	eng - English
Country	CZ - Czech Republic
Keywords	Multivariate statistics ; Mixtures of dependence trees ; EM algorithm ; Pattern recognition ; Medical image analysis
Subject RIV	IN - Informatics, Computer Science
R&D Projects	GA14-02652S GA ČR - Czech Science Foundation (CSF)
	GA14-10911S GA ČR - Czech Science Foundation (CSF)
Institutional support	UTIA-B - RVO:67985556
Annotation	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.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
Year of Publishing	2015

Number of the records: 1