Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
IDAGs: a Perfect Map for Any Distribution
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Chain graphs: semantics and expressiveness
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic Conditional Independence Structures: With 42 Illustrations (Information Science and Statistics)
A reconstruction algorithm for the essential graph
International Journal of Approximate Reasoning
Logical inference algorithms and matrix representations for probabilistic conditional independence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Logical and algorithmic properties of stable conditional independence
International Journal of Approximate Reasoning
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Efficient Algorithms for Conditional Independence Inference
The Journal of Machine Learning Research
Exploiting independencies to compute semigraphoid and graphoid structures
International Journal of Approximate Reasoning
Finding P-maps and I-maps to represent conditional independencies
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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In this article, we consider the computational aspects of deciding whether a conditional independence statement t is implied by a list of conditional independence statements L using the independence implication provided by the method of structural imsets. We present two algorithmic methods which have the interesting complementary properties that one method performs well to prove that t is implied by L, while the other performs well to prove that t is not implied by L. However, both methods do not well perform the opposite. This gives rise to a parallel algorithm in which both methods race against each other in order to determine effectively whether t is or is not implied. Some empirical evidence is provided that suggests this racing algorithms method performs considerably better than an existing method based on so-called skeletal characterization of the respective implication. Furthermore, unlike previous methods, the method is able to handle more than five variables.