Uncertainty in Artificial Intelligence
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Reading Dependencies from Polytree-Like Bayesian Networks
Jose Pena
Abstract:
We present a graphical criterion for reading dependencies from the minimal directed in- dependence map G of a graphoid p when G is a polytree and p satisfies composition and weak transitivity. We prove that the crite- rion is sound and complete. We argue that assuming composition and weak transitivity is not too restrictive.
Keywords:
Pages: 303-309
PS Link:
PDF Link: /papers/07/p303-pena.pdf
BibTex:
@INPROCEEDINGS{Pena07,
AUTHOR = "Jose Pena ",
TITLE = "Reading Dependencies from Polytree-Like Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "303--309"
}


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