d-Separation: From Theorems to Algorithms

03/27/2013 ∙ by Dan Geiger, et al. ∙ 0

An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.

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