Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Expert Systems
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Parameter Learning in Object-Oriented Bayesian Networks
Annals of Mathematics and Artificial Intelligence
A General Algorithm for Approximate Inference in Multiply Sectioned Bayesian Networks
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Bayesian biosurveillance of disease outbreaks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Iterative Multiagent Probabilistic Inference
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
A join tree probability propagation architecture for semantic modeling
Journal of Intelligent Information Systems
Mobile context inference using two-layered Bayesian networks for smartphones
Expert Systems with Applications: An International Journal
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As Bayesian networks are applied to larger and more complex problem domains, search for flexible modeling and more efficient inference methods is an ongoing effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for flexible modeling and distributed inference. Lazy propagation extends the Shafer-Shenoy and HUGIN inference methods with reduced space complexity. We apply the Shafer-Shenoy and lazy propagation to inference in MSBNs. The combination of the MSBN framework and lazy propagation provides a better framework for modeling and inference in very large domains. It retains the modeling flexibility of MSBNs and reduces the runtime space complexity, allowing exact inference in much larger domains given the same computational resources.