Polyhedral approach to statistical learning graphical models

0377257 - ÚTIA 2013 RIV SG eng C - Konferenční příspěvek (zahraniční konf.)
Studený, Milan - Haws, D. - Hemmecke, R. - Lindner, S.
Polyhedral approach to statistical learning graphical models.
Harmony of Gröbner Bases and the Modern Industrial Society. Singapore: World Scientific Press, 2012, s. 346-372. ISBN 978-981-4383-45-5.
[The 2nd CREST-SBM International Conference "Harmony of Groebner Bases and the Modern Industrial Society". Osaka (JP), 28.06.2012-2.07.2012]
Grant CEP: GA ČR GA201/08/0539
Institucionální podpora: RVO:67985556
Klíčová slova: Bayesian network stucture * standard imset * characteristic imset * polyhedral geometry
Kód oboru RIV: BA - Obecná matematika
http://library.utia.cas.cz/separaty/2012/MTR/studeny-polyhedral approach to statistical learning graphical models.pdf

The statistical task to learn graphical models of Bayesian network structure leads to the study of special polyhedra. In the paper, we offer an overview of our polyhedral approach to learning these statistical models. First, we report on the results on this topic from our recent papers. The second part of the paper brings some specific additional results inspired by this approach.
Trvalý link: http://hdl.handle.net/11104/0209464