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Most Relevant Explanation (MRE) is a method for finding multivariateexplanations for given evidence in Bayesian networks [12]. This paper studiesthe theoretical properties of MRE and develops an algorithm for findingmultiple top MRE solutions. Our study shows that MRE relies on an implicit softrelevance measure in automatically identifying the most relevant targetvariables and pruning less relevant variables from an explanation. The softmeasure also enables MRE to capture the intuitive phenomenon of explaining awayencoded in Bayesian networks. Furthermore, our study shows that the solutionspace of MRE has a special lattice structure which yields interesting dominancerelations among the solutions. A K-MRE algorithm based on these dominancerelations is developed for generating a set of top solutions that are morerepresentative. Our empirical results show that MRE methods are promisingapproaches for explanation in Bayesian networks.

Most Relevant Explanation: Properties, Algorithms, and Evaluations