A unifying formalism for the directed, and undirected graphical models How does that work then?

A factor graph is a bipartite graph representing the factorization of a function. In probability theory and its applications, factor graphs are used to represent factorization of a probability distribution function, enabling efficient computations, such as the computation of marginal distributions through the sum-product algorithm.

Hmm.

Data extraction system Deepdive uses factor graphs.

## Refs

- Frey03
- Frey, B. J.(2003) Extending factor graphs so as to unify directed and undirected graphical models. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (pp. 257–264). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
- Koch07
- Koch, V. M.(2007) A factor graph approach to model-based signal separation. . ETH Zurich, Konstanz
- KsFL01
- Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) Factor graphs and the sum-product algorithm.
*IEEE Transactions on Information Theory*, 47(2), 498–519. DOI. - Loel04
- Loeliger, H.-A. (2004) An introduction to factor graphs.
*IEEE Signal Processing Magazine*, 21(1), 28–41. DOI. - MaKF04
- Mao, Y., Kschischang, F. R., & Frey, B. J.(2004) Convolutional Factor Graphs As Probabilistic Models. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (pp. 374–381). Arlington, Virginia, United States: AUAI Press