title: Constructing probabilistic models.
abstract: Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas.
authors.affiliation: Laboratory of Intelligent Systems, Faculty of Informatics & Statistics, Prague, Czech Republic. radim@vse.cz
authors.forename: R
authors.initials: R
authors.lastname: Jirousek
authors.forename: N
authors.initials: N
authors.lastname: Kushmerick
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003628:Database Management Systems; D006801:Humans; D016571:Neural Networks, Computer; D011338:Probability Theory; D012984:Software