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Learning Bayesian network structure: towards the essential graph by integer linear programming tools
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SYSNO ASEP 0427002 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Learning Bayesian network structure: towards the essential graph by integer linear programming tools Tvůrce(i) Studený, Milan (UTIA-B) RID, ORCID
Haws, D. (US)Celkový počet autorů 2 Zdroj.dok. International Journal of Approximate Reasoning. - : Elsevier - ISSN 0888-613X
Roč. 55, č. 4 (2014), s. 1043-1071Poč.str. 29 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova learning Bayesian network structure ; integer linear programming ; characteristic imset ; essential graph Vědní obor RIV BA - Obecná matematika CEP GA13-20012S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 UT WOS 000334087400009 DOI 10.1016/j.ijar.2013.09.016 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2015
Počet záznamů: 1