(50 minutes to learn)
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.
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.
- conditional random fields
- Click on "Preview" to see the videos.
→ Probabilistic Graphical Models: Principles and Techniques
A very comprehensive textbook for a graduate-level course on probabilistic AI.
Location: Section 184.108.40.206 and boxes 4.C and 4.D, pages 124-128
Supplemental resources (the following are optional, but you may find them useful)
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
-No Additional Notes-
- create concept: shift + click on graph
- change concept title: shift + click on existing concept
- link together concepts: shift + click drag from one concept to another
- remove concept from graph: click on concept then press delete/backspace
- add associated content to concept: click the small circle that appears on the node when hovering over it
- other actions: use the icons in the upper right corner to optimize the graph placement, preview the graph, or download a json representation