Uncertainty in Artificial Intelligence
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A Scheme for Approximating Probabilistic Inference
Rina Dechter, Irina Rish
This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approximation for several tasks: finding the most probable explanation, belief updating and finding the maximum a posteriori hypothesis. We identify regions of completeness and provide preliminary empirical evaluation on randomly generated networks.
Pages: 132-141
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PDF Link: /papers/97/p132-dechter.pdf
AUTHOR = "Rina Dechter and Irina Rish",
TITLE = "A Scheme for Approximating Probabilistic Inference",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "132--141"

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