Most Relevant Explanation: Properties, Algorithms, and Evaluations
  Most Relevant Explanation (MRE) is a method for finding multivariate
explanations for given evidence in Bayesian networks [12]. This paper studies
the theoretical properties of MRE and develops an algorithm for finding
multiple top MRE solutions. Our study shows that MRE relies on an implicit soft
relevance measure in automatically identifying the most relevant target
variables and pruning less relevant variables from an explanation. The soft
measure also enables MRE to capture the intuitive phenomenon of explaining away
encoded in Bayesian networks. Furthermore, our study shows that the solution
space of MRE has a special lattice structure which yields interesting dominance
relations among the solutions. A K-MRE algorithm based on these dominance
relations is developed for generating a set of top solutions that are more
representative. Our empirical results show that MRE methods are promising
approaches for explanation in Bayesian networks.
