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

Image 1-independence is a novel concept concerned with explaining the (ir)relevance of intermediate nodes for maximum a posteriori (Image 2) computations in Bayesian networks. Building upon properties of Image 1-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 Image 1-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 Image 1-independence are also suggested.