Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Probabilistic Methods for Financial and Marketing Informatics
Probabilistic Methods for Financial and Marketing Informatics
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Modeling causal reinforcement and undermining with Noisy-AND trees
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Describing disease processes using a probabilistic logic of qualitative time
Artificial Intelligence in Medicine
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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The Noisy-Or model is convenient for describing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to arbitrary functions other than the Boolean OR function. This generalization is a useful modeling aid for construction of Bayesian networks. We illustrate with some examples including digital circuit diagnosis and network reliability analysis.