Počet záznamů: 1
Approximating Probability Densities by Mixtures of Gaussian Dependence Trees
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SYSNO ASEP 0435901 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Approximating Probability Densities by Mixtures of Gaussian Dependence Trees Tvůrce(i) Grim, Jiří (UTIA-B) RID, ORCID Celkový počet autorů 1 Zdroj.dok. Stochastic and Physical Monitoring Systems, SPMS 2014. - Praha : ČVUT, 2014 - ISBN 978-80-01-05616-5 Poč.str. 13 s. Forma vydání Tištěná - P Akce Stochastic and Physical Monitoring Systems SPMS 2014 Datum konání 23.06.2014-28.06.2014 Místo konání Malá Skála Země CZ - Česká republika Typ akce EUR Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova Multivariate statistics ; Mixtures of dependence trees ; EM algorithm ; Pattern recognition ; Medical image analysis Vědní obor RIV IN - Informatika CEP GA14-02652S GA ČR - Grantová agentura ČR GA14-10911S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2015
Počet záznamů: 1