Conditioning Methods for Exact and Approximate Inference in Causal
  Networks
  We present two algorithms for exact and approximate inference in causal
networks. The first algorithm, dynamic conditioning, is a refinement of cutset
conditioning that has linear complexity on some networks for which cutset
conditioning is exponential. The second algorithm, B-conditioning, is an
algorithm for approximate inference that allows one to trade-off the quality of
approximations with the computation time. We also present some experimental
results illustrating the properties of the proposed algorithms.
