Simplifying explanations in Bayesian belief networks

Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence . These configurations axe called explanations and in most of the approache s found in the literature, all the explanations have the same number of lit erals. In this paper we propose some criteria to simplify the explanations in such a way that the resulting configurations are still accounting for th e 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 network s.