SIMPLIFYING EXPLANATIONS IN BAYESIAN BELIEF NETWORKS
Luis M. de Campos, José Antonio Gámez, Serafı́n Moral · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2001
Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence. These configurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we propose some criteria to simplify the explanations in such a way that the resulting configurations are still accounting for the observed facts. Computational methods to perform the simplification task are also presented. Finally the algorithms are experimentally tested using a set of experiments which involves three different Bayesian belief networks.