| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0399130 |
| utime |
20240103203310.1 |
| mtime |
20140304235959.9 |
| SCOPUS |
84894653353 |
| WOS |
000343477100029 |
| DOI |
10.3233/978-1-61499-330-8-275 |
| title
(primary) (eng) |
Probabilistic Inference in BN2T Models by Weighted Model Counting |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0399129 |
| ISBN |
978-1-61499-329-2 |
| title
|
Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence |
| page_num |
275-284 |
| publisher |
| place |
Amsterdam |
| name |
IOS Press |
| year |
2013 |
|
| editor |
| name1 |
Jaeger |
| name2 |
Manfred |
|
| editor |
| name1 |
Nielsen |
| name2 |
Thomas Dyhre |
|
| editor |
| name1 |
Viappiani |
| name2 |
Paolo |
|
|
| keyword |
Bayesian networks |
| keyword |
Models of Independence of causal influence |
| keyword |
Noisy threshold models |
| keyword |
Probabilistic inference |
| keyword |
Weighted model counting |
| keyword |
Arithmetic circuits |
| author
(primary) |
| ARLID |
cav_un_auth*0101228 |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| full_dept |
Department of Decision Making Theory |
| name1 |
Vomlel |
| name2 |
Jiří |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| full_dept |
Department of Stochastic Informatics |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292670 |
| project_id |
GA13-20012S |
| agency |
GA ČR |
|
| project |
| ARLID |
cav_un_auth*0253174 |
| project_id |
GA102/09/1278 |
| agency |
GA ČR |
|
| abstract
(eng) |
Exact inference in Bayesian networks with nodes having a large parent set is not tractable using standard techniques as are the junction tree method or the variable elimination. However, in many applications, the conditional probability tbles of these nodes have certain local structure than can be exploited to make the exact inference tractable. In this paper we combine the CP tensor decomposition of probability tables with probabilistic inference using weighted model counting. The motivation for this combination is to exploit not only the local structure of some conditional probability tables but also other structural information potentialy present in the Baysian network, like determinism or context specific independence. We illustrate the proposed combination on BN2T networks -- two-layered Bayesian networks with conditional probability tables representing noisy threshold models. |
| action |
| ARLID |
cav_un_auth*0301481 |
| name |
The Scandinavian Conference on Artificial Intelligence (SCAI 2013) /12./ |
| dates |
20.11.2013-22.11.2013 |
| place |
Aalborg |
| country |
DK |
|
| RIV |
JD |
| reportyear |
2014 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0228479 |
| confidential |
S |
| arlyear |
2013 |
| mrcbU14 |
84894653353 SCOPUS |
| mrcbU34 |
000343477100029 WOS |
| mrcbU63 |
cav_un_epca*0399129 Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence 978-1-61499-329-2 275 284 Amsterdam IOS Press 2013 Frontiers in Artificial Intelligence and Applications |
| mrcbU67 |
Jaeger Manfred 340 |
| mrcbU67 |
Nielsen Thomas Dyhre 340 |
| mrcbU67 |
Viappiani Paolo 340 |
|