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.
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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