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.
Keywords: Conditioning, inference, algorithms, approximate reasoning.
PDF Link: /papers/95/p99-darwiche.pdf
AUTHOR = "Adnan Darwiche
TITLE = "Conditioning Methods for Exact and Approximate Inference in Causal Networks",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "99--107"