Learning Bayesian network structure: towards the essential graph by integer linear programming tools

0427002 - ÚTIA 2015 RIV US eng J - Journal Article
Studený, Milan - Haws, D.
Learning Bayesian network structure: towards the essential graph by integer linear programming tools.
International Journal of Approximate Reasoning. Roč. 55, č. 4 (2014), s. 1043-1071. ISSN 0888-613X. E-ISSN 1873-4731
R&D Projects: GA ČR GA13-20012S
Institutional support: RVO:67985556
Keywords : learning Bayesian network structure * integer linear programming * characteristic imset * essential graph
Subject RIV: BA - General Mathematics
Impact factor: 2.451, year: 2014
http://library.utia.cas.cz/separaty/2014/MTR/studeny-0427002.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0233079