Most Inforbable Explanations: Finding Explanations in Bayesian Networks That Are Both Probable and Informative

@inproceedings{Kwisthout2013MostIE,
  title={Most Inforbable Explanations: Finding Explanations in Bayesian Networks That Are Both Probable and Informative},
  author={Johan Kwisthout},
  booktitle={ECSQARU},
  year={2013}
}
The problems of generating candidate hypotheses and inferring the best hypothesis out of this set are typically seen as two distinct aspects of the more general problem of non-demonstrative inference or abduction. In the context of Bayesian networks the latter problem (computing most probable explanations) is well understood, while the former problem is typically left as an exercise to the modeler. In other words, the candidate hypotheses are pre-selected and hard-coded. In reality, however… CONTINUE READING
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