Reformulating Inference Problems Through Selective Conditioning
  We describe how we selectively reformulate portions of a belief network that
pose difficulties for solution with a stochastic-simulation algorithm. With
employ the selective conditioning approach to target specific nodes in a belief
network for decomposition, based on the contribution the nodes make to the
tractability of stochastic simulation. We review previous work on BNRAS
algorithms- randomized approximation algorithms for probabilistic inference. We
show how selective conditioning can be employed to reformulate a single BNRAS
problem into multiple tractable BNRAS simulation problems. We discuss how we
can use another simulation algorithm-logic sampling-to solve a component of the
inference problem that provides a means for knitting the solutions of
individual subproblems into a final result. Finally, we analyze tradeoffs among
the computational subtasks associated with the selective conditioning approach
to reformulation.
