0085999 20240103184438.720070924235959.9 12 s. cav_un_epca*0085998178-189PrahaUTIA AV ČR2007KroupaT.VejnarováJ. Bayesian networks artificial intelligence probabilistic graphical models machine learning cav_un_auth*0101228 Vomlel Jiří UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101202 Studený Milan UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. 1M0572 GA MŠk CZ cav_un_auth*0001814 2C06019 GA MŠk CZ cav_un_auth*0216518 CEZ:AV0Z10750506 This paper describes a modification of the greedy equivalence search (GES) algorithm. The presented modification is based on the algebraic approach to learning. The states of the search space are standard imsets. Each standard imset represents an equivalence class of Bayesian networks. For a given quality criterion the database is represented by the respective data imset. This allows a very simple update of a given quality criterion since the moves between states are represented by differential imsets. We exploit a direct characterization of lower and upper inclusion neighborhood, which allows an efficient search for the best structure in the inclusion neighborhood. The algorithm was implemented in R and is freely available. Článek popisuje implementaci hladového algoritmu pro učení baysovských sítí. Algoritmus je založen na algebraických objektech - tzv. imsetech a na prohledávání tzv. inkluzivního okolí. cav_un_auth*0230109 Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./ Liblice 15.09.2007-18.09.2007 CZ 2008 BB http://hdl.handle.net/11104/0148380 2007 cav_un_epca*0085998 Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./ 178 189 Praha UTIA AV ČR 2007 Kroupa T. 340 Vejnarová J. 340