The Living Thing / Notebooks :

(Probabilistic) graphical models

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.

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Barber’s (Barb12) taxonomy of 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.