# conditional random fields

(1.1 hours to learn)

## Summary

MRFs encode factorization and conditional independence structure in a joint distribution over some set of random variables. Conditional random fields (CRFs) instead directly encode the same kind of structure in the conditional distribution of one set of random variables given another. This allows them to represent more complex dependencies in the conditional distribution. They are commonly used in computer vision and natural language processing.

## Context

This concept has the prerequisites:

- Markov random fields (CRFs are defined in terms of MRFs.)

## Core resources (read/watch one of the following)

## -Free-

→ Coursera: Probabilistic Graphical Models (2013)

An online course on probabilistic graphical models.

Location:
Lecture "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 4.6.1.1 (pages 142-144), Box 4.E (pages 145-148), and Box 4.B (pages 112-114)

## See also

-No Additional Notes-