Updating Probabilities in Multiply-Connected Belief Networks
  This paper focuses on probability updates in multiply-connected belief
networks. Pearl has designed the method of conditioning, which enables us to
apply his algorithm for belief updates in singly-connected networks to
multiply-connected belief networks by selecting a loop-cutset for the network
and instantiating these loop-cutset nodes. We discuss conditions that need to
be satisfied by the selected nodes. We present a heuristic algorithm for
finding a loop-cutset that satisfies these conditions.
