Most Relevant Explanation in Bayesian Networks

@article{Yuan2011MostRE,
  title={Most Relevant Explanation in Bayesian Networks},
  author={Changhe Yuan and Heejin Lim and Tsai-Ching Lu},
  journal={J. Artif. Intell. Res.},
  year={2011},
  volume={42},
  pages={309-352}
}
A major inference task in Bayesian networks is explaining why some variables are observed in their particular states using a set of target variables. Existing methods for solving this problem often generate explanations that are either too simple (underspecified) or too complex (overspecified). In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial instantiation of the target variables that maximizes the generalized Bayes factor (GBF) as the best… CONTINUE READING

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