A Probabilistic Approach to Hierarchical Model-based Diagnosis
  Model-based diagnosis reasons backwards from a functional schematic of a
system to isolate faults given observations of anomalous behavior. We develop a
fully probabilistic approach to model based diagnosis and extend it to support
hierarchical models. Our scheme translates the functional 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.
