Factorization of Discrete Probability Distributions
Geiger, Dan, Meek, Christopher, Sturmfels, Bernd
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Dec-12-2012
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