Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
A Language for Construction of Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refinement and coarsening of Bayesian networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
On the detection of conflicts in diagnostic Bayesian networks using abstraction
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Some experiments with real-time decision algorithms
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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One important factor determining the computational complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an anytime procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases. This suggests that state-space abstraction is one more useful control parameter for designing real-time probabilistic reasoners.