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:
- Bayesian networks
- d-separation (Reasoning about representational power requires the conditional independence interpretation.)
- Markov random fields
Core resources (read/watch one of the following)
→ Coursera: Probabilistic Graphical Models (2013)
An online course on probabilistic graphical models.
Location: Lecture "I-maps and perfect maps"
- Click on "Preview" to see the videos.
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 8.3.4, pages 390-393
Supplemental resources (the following are optional, but you may find them useful)
→ Probabilistic Graphical Models: Principles and Techniques
A very comprehensive textbook for a graduate-level course on probabilistic AI.
Location: Sections 3.2.3 (pages 60-64), Section 3.4-3.4.2 (pages 78-83), and Section 4.5 (pages 134-142)
-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