  Bayesian networks offer great potential for use in automating large scale
diagnostic reasoning tasks. Gibbs sampling is the main technique used to
perform diagnostic reasoning in large richly interconnected Bayesian networks.
Unfortunately Gibbs sampling can take an excessive time to generate a
representative sample. In this paper we describe and test a number of heuristic
strategies for improving sampling in noisy-or Bayesian networks. The strategies
include Monte Carlo Markov chain sampling techniques other than Gibbs sampling.
Emphasis is put on strategies that can be implemented in distributed systems.
