Placeholder for my notes on probabilistic graphical models.
In general graphical models are a particular type of way of handling
multivariate data based on working out *what* is
conditionally independent of *what else*.

To taxonomise:

- directed graphical models, and
- undirected graphical models
- a supposed unifying formalism factor graphs
- chain graphs are cliques of undirected graphs with directed connections
- conditional random fields
- hierarchical models are special cases of directed graphical models.
- quantum graphical models because it all looks a bit different with noncommutative probability.

## Tidbits

- E.Z. graphical regression with (so-called) BayesDB.
- Stanford CS228 notes by Stefano Ermon and Volodymyr Kuleshov,

## Implementations

Pedagogically useful, although probably not industrial-grade, David Barber’s discrete graphical model code (Julia).

## Refs

- Barb12
- Barber, D. (2012) Bayesian reasoning and machine learning. . Cambridge ; New York: Cambridge University Press
- JoWe02a
- Jordan, M. I., & Weiss, Y. (2002a) Graphical models: Probabilistic inference.
*The Handbook of Brain Theory and Neural Networks*, 490–496. - JoWe02b
- Jordan, M. I., & Weiss, Y. (2002b) Probabilistic inference in graphical models.
*Handbook of Neural Networks and Brain Theory*. - Laur96
- Lauritzen, S. L.(1996) Graphical Models. . Clarendon Press
- NeOt04
- Neapolitan, R. E., & others. (2004) Learning bayesian networks. (Vol. 38). Prentice Hall Upper Saddle River
- Stud05
- Studený, M. (2005) Probabilistic conditional independence structures. . London: Springer
- WaJo08
- Wainwright, M. J., & Jordan, M. I.(2008) Graphical models, exponential families, and variational inference.
*Foundations and Trends® in Machine Learning*, 1(1–2), 1–305. DOI.