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.

• Type: Conference
• Year: 2008
• Where: FEGC
• Authors: Dan A. Simovici, Saaid Baraty
• Keywords: Biometrics, Conditional Entropy, FEGC 2008, Generalized Conditional Entropy, Shannon Conditional Entropy