Knowledge Engineering for Large Belief Networks
  We present several techniques for knowledge engineering of large belief
networks (BNs) based on the our experiences with a network derived from a large
medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is
used to model causal in dependence in a BN with multi-valued variables. We
describe the use of leak probabilities to enforce the closed-world assumption
in our model. We present Netview, a visualization tool based on causal
independence and the use of leak probabilities. The Netview software allows
knowledge engineers to dynamically view sub-networks for knowledge engineering,
and it provides version control for editing a BN. Netview generates
sub-networks in which leak probabilities are dynamically updated to reflect the
missing portions of the network.
