The EQ framework for learning equivalence classes of Bayesian networks

This paper proposes a theoretical and an algorithmic framework for the analysis and the design of efficient learning algorithms which explore the space of equivalence classes of Bayesian network structures. This framework is composed of a generic learning model which uses essential graphs and more general partially directed graphs in order to represent the equivalence classes evaluated during search, operational characterizations of these graphs, processing procedures and formulas for directly calculating their score. The experimental results of the algorithms designed within this framework show that the space of equivalence classes may be explored efficiently and with better results than the classical search in the space of Bayesian network structures.