Fusion, propagation, and structuring in belief networks
Artificial Intelligence
A valuation-based language for expert systems
International Journal of Approximate Reasoning
An efficient implementation of belief function propagation
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Graphical Belief Modeling
Local computation with valuations from a commutative semigroup
Annals of Mathematics and Artificial Intelligence
Fast-Division Architecture for Dempster-Shafer Belief Functions
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Given several Dempster-Shafer belief functions, the framework of valuation networks describes an efficient method for computing the marginal of the combined belief function. The computation is based on a message passing scheme in a Markov tree where after the selection of a root node an inward and an outward propagation can be distinguished. In this paper it will be shown that outward propagation can be replaced by another partial inward propagation. In addition it will also be shown how the efficiency of inward propagation can be improved.