Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Exploring localization in Bayesian networks for large expert systems
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Distributed Multi-Agent Probabilistic Reasoning With Bayesian Networks
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
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
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs) can be used for diagnosis of natural systems as well as for model-based diagnosis of artificial systems. They can be applied to single-agent oriented reasoning systems as well as multi-agent distributed probabilistic reasoning systems. Belief propagation between a pair of subnets plays a central role in maintenance of global consistency in a MSBN. This paper studies the operation UpdateBelief, presented originally with MSBNs, for inter-subnet propagation. We analyze how the operation achieves its intended functionality, which provides hints as for how its efficiency. New versions of UpdateBelief are then defined that reduce the computation time for inter-subnet propagation. One of them is optimal in the sense that the minimum amount of computation for coordinating multi-linkage belief propagation is required. The optimization problem is solved through the solution of a graph-theoretic problem: the minimum weight open tour in a tree.