Uncertainty in Artificial Intelligence
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d-Separation: From Theorems to Algorithms
Dan Geiger, Tom Verma, Judea Pearl
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.
Keywords: null
Pages: 139-148
PS Link:
PDF Link: /papers/89/p139-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger89,
AUTHOR = "Dan Geiger and Tom Verma and Judea Pearl",
TITLE = "d-Separation: From Theorems to Algorithms",
BOOKTITLE = "Uncertainty in Artificial Intelligence 5 Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1989",
PAGES = "139--148"
}


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