Simulation Approaches to General Probabilistic Inference on Belief
  Networks
  A number of algorithms have been developed to solve probabilistic inference
problems on belief networks. These algorithms can be divided into two main
groups: exact techniques which exploit the conditional independence revealed
when the graph structure is relatively sparse, and probabilistic sampling
techniques which exploit the "conductance" of an embedded Markov chain when the
conditional probabilities have non-extreme values. In this paper, we
investigate a family of "forward" Monte Carlo sampling techniques similar to
Logic Sampling [Henrion, 1988] which appear to perform well even in some
multiply connected networks with extreme conditional probabilities, and thus
would be generally applicable. We consider several enhancements which reduce
the posterior variance using this approach and propose a framework and criteria
for choosing when to use those enhancements.
