Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Elements of information theory
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Introduction to Bayesian Networks
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Bayesian Artificial Intelligence
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Dynamic Programming
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A Prototypical System for Soft Evidential Update
Applied Intelligence
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Knowledge representation, communication, and update in probability-based multiagent systems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Expert Systems with Applications: An International Journal
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This paper is concerned with a development of a theory on probabilistic models, and in particular Bayesian networks, when handling continuous variables. While it is possible to deal with continuous variables without discretisation, the simplest approach is to discretise them. A fuzzy partition of continuous domains will be used, which requires an inference procedure able to deal with soft evidence. Soft evidence is a type of uncertain evidence, and it is also a result of the type of discretisation used. An algorithm for inference in multiply connected networks will be proposed and exploited for filtering and abduction in dynamic, time-invariant models, when continuous variables are present.