Conditioning Methods for Exact and Approximate Inference in Causal Networks
arXiv:1302.4939
Abstract
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.
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)