Characteristic imsets for learning Bayesian network structure

0382596 - ÚTIA 2013 RIV US eng J - Journal Article
Hemmecke, R. - Lindner, S. - Studený, Milan
Characteristic imsets for learning Bayesian network structure.
International Journal of Approximate Reasoning. Roč. 53, č. 9 (2012), s. 1336-1349. ISSN 0888-613X. E-ISSN 1873-4731
R&D Projects: GA MŠMT(CZ) 1M0572; GA ČR GA201/08/0539
Institutional support: RVO:67985556
Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope
Subject RIV: BA - General Mathematics
Impact factor: 1.729, year: 2012
http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf

In this paper we introduce a new unique vector representative, called the characteristic imset, obtained from the standard imset by an affine transformation. Characteristic imsets are (shown to be) zero-one vectors and have many elegant properties, suitable for intended application of linear/integer programming methods to learning BN structure. They are much closer to the graphical description; we describe a simple transition between the characteristic imset and the essential graph, known as a traditional unique graphical representative of the BN structure. In the end, we relate our proposal to other recent approaches which apply linear programming methods in probabilistic reasoning.
Permanent Link: http://hdl.handle.net/11104/0212775