Understand
The two most popular types of graphical model are directed models (Bayesian networks) and undirected models (Markov random fields, or MRFs).
- Directed and undirected models offer complementary properties in model construction, expressing conditional independencies, expressing arbitrary factorizations of joint distributions, and formulating message-passing inference algorithms.
- We show that the strengths of these two representations can be combined in a single type of graphical model called a 'factor graph'.
- Every Bayesian network or MRF can be easily converted to a factor graph that expresses the same conditional independencies, expresses the same factorization of the joint distribution, and can be used for probabilistic inference through application of a single, simple message-passing algorithm.