Factorization of Discrete Probability Distributions

Dan Geiger, Christopher Meek, Bernd Sturmfels

UAI 2002 pp. 162-169

/uai/2002/geiger2002uai-factorization/

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.

UAI Semantic Scholar

Cite

Text

Geiger et al. "Factorization of Discrete Probability Distributions." Conference on Uncertainty in Artificial Intelligence, 2002.

Markdown

[Geiger et al. "Factorization of Discrete Probability Distributions." Conference on Uncertainty in Artificial Intelligence, 2002.](https://mlanthology.org/uai/2002/geiger2002uai-factorization/)

BibTeX

@inproceedings{geiger2002uai-factorization,
  title     = {{Factorization of Discrete Probability Distributions}},
  author    = {Geiger, Dan and Meek, Christopher and Sturmfels, Bernd},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2002},
  pages     = {162-169},
  url       = {https://mlanthology.org/uai/2002/geiger2002uai-factorization/}
}