| bibtype |
K -
Conference Paper (Czech conference)
|
| ARLID |
0493355 |
| utime |
20240111141006.0 |
| mtime |
20180917235959.9 |
| title
(primary) (eng) |
Representations of Bayesian Networks by Low-Rank Models |
| specification |
| page_count |
12 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0493354 |
| ISSN |
Proceedings of Machine Learning Research |
| title
|
Proceedings of Machine Learning Research |
| part_num |
72 |
| page_num |
463-472 |
| publisher |
| place |
Praha |
| name |
UTIA |
| year |
2018 |
|
| editor |
| name1 |
Kratochvíl |
| name2 |
Václav |
|
| editor |
| name1 |
Studený |
| name2 |
Milan |
|
|
| keyword |
canonical polyadic tensor decomposition |
| keyword |
conditional probability tables |
| keyword |
marginal probability tables |
| author
(primary) |
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101228 |
| name1 |
Vomlel |
| name2 |
Jiří |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA17-00902S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0345929 |
|
| abstract
(eng) |
Conditional probability tables (CPTs) of discrete valued random variables may achieve high dimensions and Bayesian networks defined as the product of these CPTs may become intractable by conventional methods of BN inference because of their dimensionality. In many cases, however, these probability tables constitute tensors of relatively low rank. Such tensors can be written in the so-called Kruskal form as a sum of rank-one components. Such representation would be equivalent to adding one artificial parent to all random variables and deleting all edges between the variables. The most difficult task is to find such a representation given a set of marginals or CPTs of the random variables under consideration. In the former case, it is a problem of joint canonical polyadic (CP) decomposition of a set of tensors. The latter fitting problem can be solved in a similar manner. We apply a recently proposed alternating direction method of multipliers (ADMM), which assures that the model has a probabilistic interpretation, i.e., that all elements of all factor matrices are nonnegative. We perform experiments with several well-known Bayesian networks.\n\n |
| action |
| ARLID |
cav_un_auth*0363930 |
| name |
International Conference on Probabilistic Graphical Models |
| dates |
20180911 |
| mrcbC20-s |
20180914 |
| place |
Praha |
| country |
CZ |
|
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0286997 |
| confidential |
S |
| arlyear |
2018 |
| mrcbU56 |
326 kB |
| mrcbU63 |
cav_un_epca*0493354 Proceedings of Machine Learning Research 72 UTIA 2018 Praha 463 472 1938-7228 |
| mrcbU67 |
340 Kratochvíl Václav |
| mrcbU67 |
340 Studený Milan |
|