d-Separation: From Theorems to Algorithms
classification
💻 cs.AI
keywords
algorithmd-separationnetworkalgorithmsbayesiancompletenesscorrectnessdeveloped
read the original abstract
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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.