| bibtype |
J -
Journal Article
|
| ARLID |
0427059 |
| utime |
20240103204112.8 |
| mtime |
20140618235959.9 |
| WOS |
000334087400010 |
| DOI |
10.1016/j.ijar.2013.12.002 |
| title
(primary) (eng) |
Probabilistic inference with noisy-threshold models based on a CP tensor decomposition |
| specification |
| page_count |
21 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256774 |
| ISSN |
0888-613X |
| title
|
International Journal of Approximate Reasoning |
| volume_id |
55 |
| volume |
4 (2014) |
| page_num |
1072-1092 |
| publisher |
|
|
| keyword |
Bayesian networks |
| keyword |
Probabilistic inference |
| keyword |
Candecomp-Parafac tensor decomposition |
| keyword |
Symmetric tensor rank |
| author
(primary) |
| ARLID |
cav_un_auth*0101228 |
| name1 |
Vomlel |
| name2 |
Jiří |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-20012S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292670 |
|
| project |
| project_id |
GA102/09/1278 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0253174 |
|
| abstract
(eng) |
The specification of conditional probability tables (CPTs) is a difficult task in the construction of probabilistic graphical models. Several types of canonical models have been proposed to ease that difficulty. Noisy-threshold models generalize the two most popular canonical models: the noisy-or and the noisy-and. When using the standard inference techniques the inference complexity is exponential with respect to the number of parents of a variable. More efficient inference techniques can be employed for CPTs that take a special form. CPTs can be viewed as tensors. Tensors can be decomposed into linear combinations of rank-one tensors, where a rank-one tensor is an outer product of vectors. Such decomposition is referred to as Canonical Polyadic (CP) or CANDECOMP-PARAFAC (CP) decomposition. The tensor decomposition offers a compact representation of CPTs which can be efficiently utilized in probabilistic inference. |
| reportyear |
2015 |
| RIV |
JD |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0233078 |
| confidential |
S |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.683 |
| mrcbT16-s |
1.460 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
58.39 |
| mrcbT16-C |
81.707 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2014 |
| mrcbU34 |
000334087400010 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 55 č. 4 2014 1072 1092 Elsevier |
|