| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0435901 |
| utime |
20240103205138.8 |
| mtime |
20150106235959.9 |
| title
(primary) (eng) |
Approximating Probability Densities by Mixtures of Gaussian Dependence Trees |
| specification |
| page_count |
13 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0438489 |
| ISBN |
978-80-01-05616-5 |
| title
|
Stochastic and Physical Monitoring Systems, SPMS 2014 |
| publisher |
| place |
Praha |
| name |
ČVUT |
| year |
2014 |
|
|
| keyword |
Multivariate statistics |
| keyword |
Mixtures of dependence trees |
| keyword |
EM algorithm |
| keyword |
Pattern recognition |
| keyword |
Medical image analysis |
| author
(primary) |
| ARLID |
cav_un_auth*0101091 |
| name1 |
Grim |
| name2 |
Jiří |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| share |
100 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA14-02652S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0303412 |
|
| project |
| project_id |
GA14-10911S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0303439 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0310357 |
| name |
Stochastic and Physical Monitoring Systems SPMS 2014 |
| place |
Malá Skála |
| dates |
23.06.2014-28.06.2014 |
| country |
CZ |
|
| reportyear |
2015 |
| RIV |
IN |
| num_of_auth |
1 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0241872 |
| mrcbC61 |
1 |
| confidential |
S |
| arlyear |
2014 |
| mrcbU63 |
cav_un_epca*0438489 Stochastic and Physical Monitoring Systems, SPMS 2014 978-80-01-05616-5 Praha ČVUT 2014 |
|