d-Separation: From Theorems to Algorithms
Dan Geiger, Tom S. Verma, Judea Pearl · arXiv (Cornell University) · 2013
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.