d-Separation: From Theorems to Algorithms. Geiger, D., Verma, T. S., & Pearl, J. 2013.
Paper doi abstract bibtex 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.
@misc{geiger_d-separation_2013,
title = {d-{Separation}: {From} {Theorems} to {Algorithms}},
copyright = {arXiv.org perpetual, non-exclusive license},
shorttitle = {d-{Separation}},
url = {https://arxiv.org/abs/1304.1505},
doi = {10.48550/ARXIV.1304.1505},
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.},
urldate = {2025-03-19},
publisher = {arXiv},
author = {Geiger, Dan and Verma, Tom S. and Pearl, Judea},
year = {2013},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences},
}
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