Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources
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Citation: Eric Horvitz and Henri Jacques Suermondt and Gregory F. Cooper. (1989) Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources. In Association for Uncertainty in Artificial Intelligence, 1989.
| Publication inproceedings ( Edit ) | |
| type | InProceedings |
| bibtype | inproceedings |
| Bibtex basics | |
| author | Eric Horvitz and Henri Jacques Suermondt and Gregory F. Cooper |
| title | Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources |
| booktitle | Association for Uncertainty in Artificial Intelligence |
| address | WIndsor, ON |
| year | 1989 |
| Bibtex more | |
| Access Paper | |
| abstract | 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. |
| KSL Technical Report ID: KSL-89-42 |
Facts about Bounded Conditioning: Flexible Inference for Decisions Under Scarce ResourcesRDF feed
| 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 + |
| Bibtype | inproceedings + |
| Booktitle | Association for Uncertainty in Artificial Intelligence + |
| Has author | Eric Horvitz and Henri Jacques Suermondt and Gregory F. Cooper + |
| Has identifier | KSL-89-42 + |
| 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 + |
| Ksl tr id | KSL-89-42 + |
| Process note | NO + |
| Title | Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources + |
| Year | 1989 + |
