Initialization for the method of conditioning in Bayesian belief networks

A method is presented that lets us compute the joint prior probabilities of the nodes of the loop cutset by instantiating the loop-cutset nodes sequentially.

Abstract

The method of conditioning allows us to use Pearl's probabilistic-inference algorithm in multiply connected belief networks by instantiating a subset of the nodes in the network, the loop cutset. To use the method of conditioning, we must calculate the joint prior probabilities of the nodes of the loop cutset. We present a method that lets us compute these joint priors by instantiating the loop-cutset nodes sequentially.