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.

To discuss: relation to message passing via factor graph decompositions? e.g. via (Cox, van de Laar, and de Vries 2019). Forney-vs-classic-style factor graphs etc.

## References

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*Journal of Machine Learning Research*7 (December): 1743β88.
Cox, Marco, Thijs van de Laar, and Bert de Vries. 2019. βA Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms.β

*International Journal of Approximate Reasoning*104 (January): 185β204.
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Loeliger, H.-A. 2004. βAn Introduction to Factor Graphs.β

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