# 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-