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Factor graphs

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.


Data extraction system Deepdive uses factor graphs.


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.
Koch, V. M.(2007) A factor graph approach to model-based signal separation. . ETH Zurich, Konstanz
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.
Loeliger, H.-A. (2004) An introduction to factor graphs. IEEE Signal Processing Magazine, 21(1), 28–41. DOI.
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