| bibtype |
J -
Journal Article
|
| ARLID |
0427002 |
| utime |
20240103204108.8 |
| mtime |
20140618235959.9 |
| WOS |
000334087400009 |
| DOI |
10.1016/j.ijar.2013.09.016 |
| title
(primary) (eng) |
Learning Bayesian network structure: towards the essential graph by integer linear programming tools |
| specification |
| page_count |
29 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 |
1043-1071 |
| publisher |
|
|
| keyword |
learning Bayesian network structure |
| keyword |
integer linear programming |
| keyword |
characteristic imset |
| keyword |
essential graph |
| author
(primary) |
| ARLID |
cav_un_auth*0101202 |
| name1 |
Studený |
| name2 |
Milan |
| 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*0274176 |
| name1 |
Haws |
| name2 |
D. |
| country |
US |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-20012S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292670 |
|
| abstract
(eng) |
The basic idea of the geometric approach to learning a Bayesian network (BN) structure is to represent every BN structure by a certain vector. If the vector representative is chosen properly, it allows one to re-formulate the task of finding the global maximum of a score over BN structures as an integer linear programming (ILP) problem. Such a suitable zero-one vector representative is the characteristic imset, introduced by Studený, Hemmecke and Lindner in 2010, in the proceedings of the 5th PGM workshop. In this paper, extensions of characteristic imsets are considered which additionally encode chain graphs without flags equivalent to acyclic directed graphs. The main contribution is a polyhedral description of the respective domain of the ILP problem, that is, by means of a set of linear inequalities. |
| reportyear |
2015 |
| RIV |
BA |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0233079 |
| 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 |
000334087400009 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 55 č. 4 2014 1043 1071 Elsevier |
|