@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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