inference in MRFs

(1.4 hours to learn)


One reason we build graphical models is so we can perform inference, i.e. ask questions about the distribution. The most common queries include: (1) finding the marginal distribution of one or several nodes, (2) finding the most likely joint assignment, or (3) computing the partition function. Items (1) and (3) are closely related. While exact inference is intractable in the general case, there are powerful approximate inference algorithms, as well as interesting classes of tractable models.


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
Other notes:
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See also