0435901 20240103205138.820150106235959.9 13 s. P cav_un_epca*0438489978-80-01-05616-5PrahaČVUT2014 Multivariate statistics Mixtures of dependence trees EM algorithm Pattern recognition Medical image analysis cav_un_auth*0101091 Grim Jiří Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition 100 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/RO/grim-0435901.pdf GA14-02652S GA ČR CZ cav_un_auth*0303412 GA14-10911S GA ČR CZ cav_un_auth*0303439 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. cav_un_auth*0310357 Stochastic and Physical Monitoring Systems SPMS 2014 Malá Skála 23.06.2014-28.06.2014 CZ 2015 IN 1 PR RVO:67985556 http://hdl.handle.net/11104/0241872 1 S 2014 cav_un_epca*0438489 Stochastic and Physical Monitoring Systems, SPMS 2014 978-80-01-05616-5 Praha ČVUT 2014