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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	Learning Bayesian network structure: towards the essential graph by integer linear programming tools Learning Bayesian network structure: towards the essential graph by integer linear programming tools (en)
skos:prefLabel	Learning Bayesian network structure: towards the essential graph by integer linear programming tools Learning Bayesian network structure: towards the essential graph by integer linear programming tools (en)
skos:notation	RIV/67985556:_____/14:00427002!RIV15-GA0-67985556
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GA13-20012S)
http://linked.open...iv/cisloPeriodika	4
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Studený, Milan
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	25838
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/14:00427002
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	learning Bayesian network structure; integer linear programming; characteristic imset; essential graph (en)
http://linked.open.../riv/klicoveSlovo	integer linear programming characteristic imset essential graph learning Bayesian network structure
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[A20ACBB6ACA1]
http://linked.open...i/riv/nazevZdroje	International Journal of Approximate Reasoning
http://linked.open...in/vavai/riv/obor	BA
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Conditional independence structures: algebraic and geometric methods
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	55
http://linked.open...iv/tvurceVysledku	Studený, Milan Haws, D.
http://linked.open...ain/vavai/riv/wos	000334087400009
issn	0888-613X
number of pages	29 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.ijar.2013.09.016

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