Uncertainty in Artificial Intelligence
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Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
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.
Pages: 162-169
PS Link:
PDF Link: /papers/02/p162-geiger.pdf
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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