Logical inference algorithms for conditional independence (CI) statements have important applications from testing consistency during knowledge elicitation to constraint-based structure learning of graphical models. Cis-inference is an approximate logical inference algorithm which combines a falsification and a novel validation algorithm. The validation algorithm represents each set of CI statements as a sparse 0-1 matrix A and validates instances of the implication problem by solving specific linear programs with constraint matrix A. It has been shown experimentally that the algorithm is both effective and efficient in validating and falsifying instances of the probabilistic CI implication problem.
This is the code used for the experiments reported on in the paper "Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence" published in the Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.
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