Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Predicting carcinoid heart disease with the noisy-threshold classifier
Artificial Intelligence in Medicine
A generic qualitative characterization of independence of causal influence
International Journal of Approximate Reasoning
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
The jabberwocky programming environment for structured social computing
Proceedings of the 24th annual ACM symposium on User interface software and technology
A qualitative characterisation of causal independence models using boolean polynomials
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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
Simultaneous decision networks with multiple objectives as support for strategic planning
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Artificial Intelligence in Medicine
Non-impeding noisy-AND tree causal models over multi-valued variables
International Journal of Approximate Reasoning
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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Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian networks and proposed three models for the representation of conditional probabilities. A forth model, shown here, assumes that the parameter distribution is given by a product of Gaussian functions and updates them from the λ and π messages of evidence propagation. We also generalize the noisy OR-gate for multivalued variables, develop the algorithm to compute probability in time proportional to the number of parents (even in networks with loops) and apply the learning model to this gate.