A Probabilistic Approach to Hierarchical Model-based Diagnosis
نویسنده
چکیده
Model-based diagnosis reasons backwards from a functional schematic of a system to isolate faults given observations of anoma lous behavior. We develop a fully proba bilistic approach to model based diagno sis and extend it to support hierarchical models. Our scheme translates the func tional schematic into a Bayesian network and diagnostic inference takes place in the Bayesian network. A Bayesian network diagnostic inference algorithm is modified to take advantage of the hierarchy to give computational gains.
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