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.

Duplicate Docs Excel Report

Title
None found

Similar Docs  Excel Report  more

TitleSimilaritySource
None found