| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0106358 |
| utime |
20240103173138.2 |
| mtime |
20050324235959.9 |
| title
(primary) (eng) |
What is the difference between Bayesian networks and compositional models? |
| specification |
|
| serial |
| title
|
Proceedings of the 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty |
| page_num |
191-196 |
| publisher |
| place |
Awaji |
| name |
Osaka University |
| year |
2004 |
|
| editor |
|
| editor |
|
| editor |
|
|
| title
(cze) |
Jaký je rozdíl mezi bayesovskými sítěmi a kompozicionálními modely |
| keyword |
probability |
| keyword |
multidimensional distribution |
| keyword |
graphical Markov model |
| author
(primary) |
| ARLID |
cav_un_auth*0101118 |
| name1 |
Jiroušek |
| name2 |
Radim |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| COSATI |
12A |
| cas_special |
| project |
| project_id |
IAA2075302 |
| agency |
GA AV ČR |
| ARLID |
cav_un_auth*0001801 |
|
| project |
| project_id |
2004/19 |
| agency |
AKTION |
| country |
CZ |
| ARLID |
cav_un_auth*0200692 |
|
| research |
CEZ:AV0Z1075907 |
| abstract
(eng) |
In this contribution we discuss a relation of two types of multidimensional models introduced within the framework of probability theory, which appeared to be in a sense equivalent: Bayesian networks and compositional models. Based on a simple example we analyse algorithms transforming one type of the model into the other. In this way we demonstrate a principal difference, which explains why the compositional models are more efficient for computations. |
| abstract
(cze) |
Příspěvek diskutuje vztah mezi dvěma typy pravděpodobnostních modelů, které jsou v jistém smyslu ekvivalentní: mezi bayesovskými sítěmi a kompozicionálními modely. Na základě jednoduchého příkladu jsou analyzovany rozdíly v algoritmech převádějících jeden model na druhý. Na tomto příkladu je ukázán základní rozdíl, který vysvětluje skutečnost, že kompozicionální modely mohou být z výpočetního hlediska efektivnější než bayesovské sítě |
| action |
| ARLID |
cav_un_auth*0129884 |
| name |
Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /7./ |
| dates |
31.08.2004-02.09.2004 |
| place |
Awaji |
| country |
JP |
|
| RIV |
BA |
| reportyear |
2005 |
| permalink |
http://hdl.handle.net/11104/0013540 |
| ID_orig |
UTIA-B 20040170 |
| arlyear |
2004 |
| mrcbU63 |
Proceedings of the 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty Osaka University 2004 Awaji 191 196 |
| mrcbU67 |
Noguchi H. 340 |
| mrcbU67 |
Ishii H. 340 |
| mrcbU67 |
Inuiguchi M. 340 |
|