converting between graphical models

(40 minutes to learn)


Bayes nets and MRFs are two frameworks for specifying factorization and conditional independence structure in probabilistic models. There are transformations which convert from one graphical model formalism to the other. However, sometimes these transformations must lose precision, because there are sets of independencies which can be represented as Bayes nets but not MRFs, and vice versa.


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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Supplemental resources (the following are optional, but you may find them useful)


See also

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