Uncertainty in Artificial Intelligence
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Global Conditioning for Probabilistic Inference in Belief Networks
Ross Shachter, Stig Andersen, Peter Szolovits
In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loopcutset conditioning. We show that global conditioning, as well as loop-cutset conditioning, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (199Oa; 1990b). Nonetheless, this approach provides new opportunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) combining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully.
Keywords: Causality, belief networks, causal networks, planning under uncertainty, troubleshoot
Pages: 514-522
PS Link:
PDF Link: /papers/94/p514-shachter.pdf
AUTHOR = "Ross Shachter and Stig Andersen and Peter Szolovits",
TITLE = "Global Conditioning for Probabilistic Inference in Belief Networks",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "514--522"

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