Markov random fields

(2 hours to learn)


Markov random fields (MRFs) are a kind of probabilistic model which encodes the model structure as an undirected graph. Two variables are connected by an edge if they directly influence each other. MRFs are useful for domains which can be described in terms of "soft constraints" between variables. MRFs can be equivalently characterized in terms of factorization of the joint distribution or conditional independence properties.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Coursera: Probabilistic Graphical Models (2013)
An online course on probabilistic graphical models.
Author: Daphne Koller
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Supplemental resources (the following are optional, but you may find them useful)


See also