Artificial Intelligence
Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Distributed revision of composite beliefs
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Incremental construction and evaluation of defeasible probabilistic models
International Journal of Approximate Reasoning
Local expression languages for probabilistic dependence: a preliminary report
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Advances in probabilistic reasoning
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Search-based methods to bound diagnostic probabilities in very large belief nets
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A general framework for reason maintenance
Artificial Intelligence
Process, structure and modularity in reasoning with uncertainty
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
On the expressiveness of rule-based systems for reasoning with uncertainty
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A probabilistic model of plan recognition
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Focusing on probable diagnoses
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Importance sampling algorithms for Bayesian networks: Principles and performance
Mathematical and Computer Modelling: An International Journal
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Propositional representation services such as truth maintenance systems offer powerful support for incremental, interleaved, problem-model construction and evaluation. Probabilistic inference systems, in contrast, have lagged behind in supporting this incrementality typically demanded by problem-solvers. The problem, we argue, is that the basic task of probabilistic inference is typically formulated at too large a grain-size. We show how a system built around a smaller grain-size inference task can have the desired incrementality and serve as the basis for a low-level (propositional) probabilistic representation service.