Structure Inference of Bayesian Networks from Data: A New Approach Based on Generalized Conditional Entropy

We propose a novel algorithm for extracting the structure of a Bayesian network from a dataset. Our approach is based on generalized conditional entropies, a parametric family of entropies that extends the usual Shannon conditional entropy. Our results indicate that with an appropriate choice of a generalized conditional entropy we obtain Bayesian networks that have superior scores compared to similar structures obtained by classical inference methods.