Uncertainty in Artificial Intelligence
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Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
Abstract:
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general exponential models. This result generalizes the well known Hammersley-Clifford Theorem.
Keywords:
Pages: 162-169
PS Link:
PDF Link: /papers/02/p162-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger02,
AUTHOR = "Dan Geiger and Christopher Meek and Bernd Sturmfels",
TITLE = "Factorization of Discrete Probability Distributions",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "162--169"
}


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