| bibtype |
M -
Monography Chapter
|
| ARLID |
0473188 |
| utime |
20240103213856.7 |
| mtime |
20170317235959.9 |
| SCOPUS |
85029310283 |
| WOS |
000403116600007 |
| DOI |
10.1090/conm/685/13751 |
| title
(primary) (eng) |
Polyhedral approaches to learning Bayesian networks |
| specification |
| book_pages |
277 |
| page_count |
34 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0473187 |
| ISBN |
978-1-4704-3743-5 |
| title
|
Algebraic and Geometric Methods in Discrete Mathematics |
| part_title |
Contemporary Mathematics |
| page_num |
155-188 |
| publisher |
| place |
Providence |
| name |
American Mathematical Society |
| year |
2017 |
|
| editor |
| name1 |
Harrington |
| name2 |
H. A. |
|
| editor |
|
| editor |
|
|
| keyword |
learning Bayesian networks |
| keyword |
family-variable polytope |
| keyword |
characteristic-imset polytope |
| author
(primary) |
| ARLID |
cav_un_auth*0274176 |
| name1 |
Haws |
| name2 |
D. |
| country |
US |
|
| author
|
| ARLID |
cav_un_auth*0332730 |
| name1 |
Cussens |
| name2 |
J. |
| country |
GB |
|
| author
|
| ARLID |
cav_un_auth*0101202 |
| name1 |
Studený |
| name2 |
Milan |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292670 |
| project_id |
GA13-20012S |
| agency |
GA ČR |
|
| abstract
(eng) |
Learning Bayesian network structure is the NP-hard task of finding a directed acyclic graph that best fits real data. Two integer vector encodings exist – family variable and characteristic imset – which model the solution space of BN structure. Each encoding yields a polytope, the family variable and characteristic imset polytopes respectively. It has been shown that learning BN structure using a decomposable and score equivalent scoring criteria (such as BIC) is equivalent to optimizing a linear function over either the family-variable or characteristic imset polytope. This monograph is primarily intended for readers already familiar with BN but not familiar with polyhedral approaches to learning BN. Thus, this monograph focuses on the family-variable and characteristic imset polytopes, their known faces and facets, and more importantly, deep connections between their faces and facets. Specifically that many of the faces of the family variable polytope are superfluous when learning BN structure. Sufficient background on Bayesian networks, graphs, and polytopes are provided. The currently known faces and facets of each polytope are described. Deep connections between many of the faces and facets of family-variable and characteristic polytope are then summarized from recent results. Lastly, a brief history and background on practical approaches to learning BN structure using integer linear programming over both polytopes is provided. |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10101 |
| reportyear |
2018 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0271363 |
| confidential |
S |
| mrcbC83 |
RIV/67985556:_____/17:00473188!RIV18-AV0-67985556 191975631 Doplnění UT WOS a Scopus |
| mrcbC83 |
RIV/67985556:_____/17:00473188!RIV18-GA0-67985556 191964990 Doplnění UT WOS a Scopus |
| mrcbC86 |
n.a. Proceedings Paper Mathematics Applied|Mathematics |
| mrcbC86 |
3+4 Proceedings Paper Mathematics Applied|Mathematics |
| mrcbC86 |
3+4 Proceedings Paper Mathematics Applied|Mathematics |
| arlyear |
2017 |
| mrcbU14 |
85029310283 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000403116600007 WOS |
| mrcbU63 |
cav_un_epca*0473187 Algebraic and Geometric Methods in Discrete Mathematics American Mathematical Society 2017 Providence 155 188 978-1-4704-3743-5 Contemporary Mathematics 685 |
| mrcbU67 |
340 Harrington H. A. |
| mrcbU67 |
340 Omar M. |
| mrcbU67 |
340 Wright M. |
|