KSL-89-42
## Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

**Reference: **
Horvitz, E. J.;
Suermondt, H. J.; &
Cooper, G. F. Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources. Association for Uncertainty in Artificial Intelligence, WIndsor, ON, 1989.

**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.

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