Computing probabilities of events in Bayesian networks

Authors:
Rolf Haenni;Jürg Kohlas;Norbert Lehmann
Affiliations:
Computer Science Department, University of California, Los Angeles, CA;Department of Informatics, University of Fribourg, Switzerland;Department of Informatics, University of Fribourg, Switzerland
Venue:
Technologies for constructing intelligent systems
Year:
2002

Citing 3
Cited 0

Axioms for probability and belief-function propagation

Readings in uncertain reasoning
A comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer architectures for computing marginals of probability distributions

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Binary join trees

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new approach for computing probabilities of events in Bayesian networks. The idea is to replace the outward phase of the propagation algorithm by a second (partial) inward propagation phase. The benefit of this idea is that the attention can be focussed on optimizing the inward phase.