Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
Management Science
Mixtures of Truncated Exponentials in Hybrid Bayesian Networks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Approximate probability propagation with mixtures of truncated exponentials
International Journal of Approximate Reasoning
Using test plans for Bayesian modeling
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Automating computer bottleneck detection with belief nets
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning bayesian networks structure with continuous variables
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Penniless propagation with mixtures of truncated exponentials
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Paper: Specification of models in large expert systems based on causal probabilistic networks
Artificial Intelligence in Medicine
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An extension of the expert system shell known as handling uncertainty by general influence networks (HUGIN) to include continuous variables, in the form of linear additive normally distributed variables, is presented. The theoretical foundation of the method was developed by S.L. Lauritzen, whereas this report primarily focus on implementation aspects. The approach has several advantages over purely discrete systems. It enables a more natural model of of the domain in question, knowledge acquisition is eased, and the complexity of belief revision is most often reduced considerably.