log-linear MRFs
(50 minutes to learn)
Summary
Often, when we use MRFs, we want to assign a particular functional form to the cliques. A common choice is a log-linear representation, where the potentials are log-linear functions of the model parameters. Boltzmann machines and Gaussian MRFs are probably the most common examples.
Context
This concept has the prerequisites:
Core resources (read/watch one of the following)
-Free-
→ Coursera: Probabilistic Graphical Models (2013)
An online course on probabilistic graphical models.
Additional dependencies:
- conditional random fields
Other notes:
- Click on "Preview" to see the videos.
-Paid-
→ Probabilistic Graphical Models: Principles and Techniques
A very comprehensive textbook for a graduate-level course on probabilistic AI.
Location:
Section 4.4.1.2 and boxes 4.C and 4.D, pages 124-128
Supplemental resources (the following are optional, but you may find them useful)
-Paid-
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
See also
-No Additional Notes-