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*.

Thematically, this is scattered across graphical models in inference, learning graphs from data, learning causation from data plus graphs, quantum graphical models because it all looks a bit different with noncommutative probability.

See also diagramming graphical models.

## Refs

- Barb12
- Barber, D. (2012) Bayesian reasoning and machine learning. . Cambridge ; New York: Cambridge University Press
- Bish06
- Bishop, C. M.(2006) Pattern recognition and machine learning. . New York: Springer
- Dawi79
- Dawid, A. P.(1979) Conditional independence in statistical theory.
*Journal of the Royal Statistical Society. Series B (Methodological)*, 41(1), 1–31. - Dawi80
- Dawid, A. P.(1980) Conditional Independence for Statistical Operations.
*The Annals of Statistics*, 8(3), 598–617. DOI. - Jord99
- Jordan, M. I.(1999) Learning in graphical models. . Cambridge, Mass.: MIT Press
- KoFr09
- Koller, D., & Friedman, N. (2009) Probabilistic graphical models : principles and techniques. . Cambridge, MA: MIT Press
- Laur96
- Lauritzen, S. L.(1996) Graphical Models. . Clarendon Press
- Pear08
- Pearl, J. (2008) Probabilistic reasoning in intelligent systems: networks of plausible inference. (Rev. 2. print., 12. [Dr.].). San Francisco, Calif: Kaufmann
- Pear09
- Pearl, J. (2009) Causality: Models, Reasoning and Inference. . Cambridge University Press
- PeGV89
- Pearl, J., Geiger, D., & Verma, T. (1989) Conditional independence and its representations.
*Kybernetika*, 25(7), 33–44.