| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0524580 |
| utime |
20240103224108.1 |
| mtime |
20200528235959.9 |
| SCOPUS |
85083664054 |
| DOI |
10.1109/ICCAIRO47923.2019.00023 |
| title
(primary) (eng) |
Dynamic Mixture Ratio Model |
| part_num |
92-99 |
| publisher |
| name |
The Institute of Electrical and Electronics Engineers, Inc. |
| pub_time |
2020 |
|
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0524582 |
| ISBN |
978-1-7281-3573-1 |
| title
|
Proceedings of the 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) |
| page_num |
92-99 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2020 |
|
|
| keyword |
dynamic systems |
| keyword |
mixture models |
| keyword |
Bayesian learning |
| keyword |
mixture ratio |
| author
(primary) |
| ARLID |
cav_un_auth*0333672 |
| name1 |
Ruman |
| name2 |
Marko |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| country |
SK |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101124 |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
LTC18075 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0372050 |
|
| project |
| project_id |
CA16228 |
| agency |
The European Cooperation in Science and Technology (COST) |
| country |
XE |
| ARLID |
cav_un_auth*0372051 |
|
| abstract
(eng) |
Finite mixtures of probability densities with components from exponential family serve as flexible parametric models of high-dimensional systems. However, with a few specialized exceptions, these dynamic models assume data-independent weights of mixture components. Their use is illogical and restricts the modeling applicability. The requirement for closeness with respect to conditioning, the basic learning operation, leads to a novel class of models: the mixture ratios. The paper justified them and shows their ability to model truly dynamic systems. |
| action |
| ARLID |
cav_un_auth*0392578 |
| name |
International Conference on Control, Artificial Intelligence, Robotics & Optimization ICCAIRO 2019 |
| dates |
20191208 |
| mrcbC20-s |
20191210 |
| place |
Athens |
| country |
GR |
|
| RIV |
BD |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2021 |
| num_of_auth |
2 |
| mrcbC52 |
4 A sml 4as 20231122144930.9 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0308930 |
| confidential |
S |
| contract |
| name |
Copyright |
| date |
20200130 |
|
| article_num |
19510399 |
| arlyear |
2020 |
| mrcbTft |
\nSoubory v repozitáři: karny-0524580-CopyrightReceipt.pdf |
| mrcbU02 |
C |
| mrcbU10 |
2020 |
| mrcbU10 |
The Institute of Electrical and Electronics Engineers, Inc. |
| mrcbU14 |
85083664054 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0524582 Proceedings of the 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) 978-1-7281-3573-1 92 99 Piscataway IEEE 2020 |
|