Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

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Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources +  Has identifier

Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources +  Ksl tr id

Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

Bibtype  inproceedings

Has publishing details  1989

Has title  Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

Has where published  Association for Uncertainty in Artificial Intelligence

Has year  1989

Title  Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

Year  1989

Abstract  We introduce an incremental-refinement app We introduce an incremental-refinement approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on probabilities in a belief network with computation, and converges on a final probability of interest with the allocation of a complete resource fraction. As such, the approach holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm can solve a great portion of a probabilistic bounding problem in complex belief networks through breaking the world into a set of mutually exclusive, tractable subproblems and ordering their solution by the expected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its worst-case characterization, and present its performance on a complex belief network for reasoning about problems in the intensive-care unit. about problems in the intensive-care unit.

Address  WIndsor, ON +

Author  Eric Horvitz and Henri Jacques Suermondt and Gregory F. Cooper +

Booktitle  Association for Uncertainty in Artificial Intelligence +

Has author  Eric Horvitz and Henri Jacques Suermondt and Gregory F. Cooper +

Has identifier  Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources +

Ksl tr id  Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources +

Process note  NO +

Categories  InProceedings +, KSL Technical Report +, Publication +

 

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