2013/03/27 by Dan Geiger, Geiger, Dan, Tom S. Verma +3
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge
paper · pdf · doi:10.48550/arxiv.1304.1505
openalex publication_date 2013/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.