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*.

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.

## Refs

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*Journal of the Royal Statistical Society. Series B (Methodological)*, 41(1), 1–31. - Dawi80
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*The Annals of Statistics*, 8(3), 598–617. DOI. - Jord99
- Jordan, M. I.(1999) Learning in graphical models. . Cambridge, Mass.: MIT Press
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- Pearl, J., Geiger, D., & Verma, T. (1989) Conditional independence and its representations.
*Kybernetika*, 25(7), 33–44.