Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Subjective bayesian methods for rule-based inference systems
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Incremental probabilistic inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Investigation of variances in belief networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
From certainty factors to belief networks
Artificial Intelligence in Medicine
Paper: Multiply sectioned Bayesian networks for neuromuscular diagnosis
Artificial Intelligence in Medicine
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We demonstrate that classes of dependencies among beliefs held with uncertainty cannot be represented in rule-based systems in a natural or efficient manner. We trace these limitations to a fundamental difference between certain and uncertain reasoning. In particular, we show that beliefs held with certainty are more modular than uncertain beliefs. We argue that the limitations of the rule-based approach for expressing dependencies are a consequence of forcing nonmodular knowledge into a representation scheme originally designed to represent modular beliefs. Finally, we describe a representation technique that is related, to the rule-based framework yet is not limited in the types of dependencies that it can represent.