Approximating Bayesian Belief Networks by Arc Removal

Bayesian belief networks or causal probabilistic networks may reach a certain size and complexity where the computations involved in exact probabilistic inference on the network tend to become rather time consuming. Methods for approximating a network by a simpler one allow the computational complexity of probabilistic inference on the network to be reduced at least to some extend. We propose a general framework for approximating Bayesian belief networks based on model simpliication by arc removal. The approximation method aims at reducing the computational complexity of probabilistic inference on a network at the cost of introducing a bounded error in the prior and posterior probabilities inferred. We present a practical approximation scheme and give some preliminary results.