Probabilistic reasoning in intelligent systems: networks of plausible inference
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Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Reduced MVDs and minimal covers
ACM Transactions on Database Systems (TODS)
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Journal of the ACM (JACM)
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Journal of the ACM (JACM)
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Journal of the ACM (JACM)
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UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A Structural Characterization of DAG-Isomorphic Dependency Models
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Triangulation of Bayesian Networks: A Relational Database Perspective
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Structural Characterization of DAG-Isomorphic Dependency Models
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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To design a probabilistic reasoning system, it might be necessary to construct the graphical structure of probabilistic network, from a given set of conditional independencies (CIs). It should be emphasized that certain redundant CIs must be removed before applying construction methods and algorithms. In this paper, firstly we discuss how to remove redundant CIs from a given set of CIs with the same context, which results in a reduced cover for the input CIs set, and then we suggest an algorithm to remove redundancy from arbitrary input CIs set. The resulting set of CIs after such a 'clean' procedure is unique. The conflicting CIs can be easily identified and removed, if necessary, so as to construct the desired graphical structure.