Uncertainty in Artificial Intelligence
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Reformulating Inference Problems Through Selective Conditioning
Paul Dagum, Eric Horvitz
Abstract:
We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algorithm. With employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the contribution the nodes make to the tractability of stochastic simulation. We review previous work on BNRAS algorithms- randomized approximation algorithms for probabilistic inference. We show how selective conditioning can be employed to reformulate a single BNRAS problem into multiple tractable BNRAS simulation problems. We discuss how we can use another simulation algorithm-logic sampling-to solve a component of the inference problem that provides a means for knitting the solutions of individual subproblems into a final result. Finally, we analyze tradeoffs among the computational subtasks associated with the selective conditioning approach to reformulation.
Keywords:
Pages: 49-54
PS Link:
PDF Link: /papers/92/p49-dagum.pdf
BibTex:
@INPROCEEDINGS{Dagum92,
AUTHOR = "Paul Dagum and Eric Horvitz",
TITLE = "Reformulating Inference Problems Through Selective Conditioning",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "49--54"
}


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