Structure Inference of Bayesian Networks from Data: A New Approach Based on Generalized Conditional Entropy.
Dan A. Simovici, Saaid Baraty · 2008
Abstract. We propose a novel algorithm for extracting the structure of a Bayesian network from a dataset. Our approach is based on generalized conditional en-tropies, a parametric family of entropies that extends the usual Shannon condi-tional entropy. Our results indicate that with an appropriate choice of a general-ized conditional entropy we obtain Bayesian networks that have superior scores compared to similar structures obtained by classical inference methods. 1