Robust Parameter Learning in Bayesian Networks with Missing Data
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:399-406, 1997.
Bayesian Belief Networks (BBNs) are a powerful formalism for knowledge representation and reasoning under uncertainty. During the past few years, Artificial Intelligence met·Statistics in the quest to develop effective methods to learn BBNs directly from databases. Unfortunately, real-world databases include missing and/or unreported data whose presence challenges traditional learning techniques, from both the theoretical and computational point of view. This paper introduces a new method to learn the probabilities defining a BBNs from databases with missing data. The intuition behind this method is close to the robust sensitivity analysis interpretation of probability: the method computes the extreme points of the set of possible distributions consistent with the available information and proceeds by refining this set as more information becomes available. This paper outlines the description of this method and presents some experimental results comparing this approach to the Gibbs Samplings.