0377257 20240103200923.520120608235959.9 10.1142/9789814383462_0020 27 s. P cav_un_epca*0377400978-981-4383-45-5346-372SingaporeWorld Scientific Press2012 Bayesian network stucture standard imset characteristic imset polyhedral geometry cav_un_auth*0101202 Studený Milan Matematická teorie rozhodování Department of Decision Making Theory MTR MTR UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0274176 Haws D. US cav_un_auth*0261765 Hemmecke R. DE cav_un_auth*0268009 Lindner S. DE http://library.utia.cas.cz/separaty/2012/MTR/studeny-polyhedral approach to statistical learning graphical models.pdf GA201/08/0539 GA ČR cav_un_auth*0239648 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. cav_un_auth*0281408 The 2nd CREST-SBM International Conference "Harmony of Groebner Bases and the Modern Industrial Society" Osaka 28.06.2012-2.07.2012 JP 2013 BA 4 RVO:67985556 http://hdl.handle.net/11104/0209464 2012 cav_un_epca*0377400 Harmony of Gröbner Bases and the Modern Industrial Society 978-981-4383-45-5 346 372 Singapore World Scientific Press 2012