Approximations in Bayesian Belief Universe for Knowledge Based Systems
  When expert systems based on causal probabilistic networks (CPNs) reach a
certain size and complexity, the "combinatorial explosion monster" tends to be
present. We propose an approximation scheme that identifies rarely occurring
cases and excludes these from being processed as ordinary cases in a CPN-based
expert system. Depending on the topology and the probability distributions of
the CPN, the numbers (representing probabilities of state combinations) in the
underlying numerical representation can become very small. Annihilating these
numbers and utilizing the resulting sparseness through data structuring
techniques often results in several orders of magnitude of improvement in the
consumption of computer resources. Bounds on the errors introduced into a
CPN-based expert system through approximations are established. Finally,
reports on empirical studies of applying the approximation scheme to a
real-world CPN are given.
