Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Operations Research
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
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Probabilistic recognition networks: an application of influence diagrams to visual recognition
Probabilistic recognition networks: an application of influence diagrams to visual recognition
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Model-Based Influence Diagrams for Machine Vision
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Node aggregation for distributed inference in Bayesian networks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
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Certain classes of problems, including perceptual data understanding, robotics, discovery, and learning, can be represented as incremental, dynamically constructed belief networks. These automatically constructed networks can be dynamically extended and modified as evidence of new individuals becomes available. The main result of this paper is the incremental extension of the singly connected polytree network in such a way that the network retains its singly connected polytree structure after the changes. The algorithm is deterministic and is guaranteed to have a complexity of single node addition that is at most of order proportional to the number of nodes (or size) of the network. Additional speed-up can be achieved by maintaining the path information. Despite its incremental and dynamic nature, the algorithm can also be used for probabilistic inference in belief networks in a fashion similar to other exact inference algorithms.