Efficient search for relevance explanations using MAP-independence in Bayesian networks

Enrique Valero-Leal, Concha Bielza, Pedro Larrañaga, Silja Renooij

Abstract: -independence is a novel concept concerned with explaining the (ir)relevance of intermediate nodes for maximum a posteriori () computations in Bayesian networks. Building upon properties of -independence, we introduce and experiment with methods for finding sets of relevant nodes using both an exhaustive and a heuristic approach. Our experiments show that these properties significantly speed up run time for both approaches. In addition, we link -independence to defeasible reasoning, a type of reasoning that analyses how new evidence may invalidate an already established conclusion. Ways to present users with an explanation using -independence are also suggested.