(Barber 2012):

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

Barber, David. 2012. *Bayesian Reasoning and Machine Learning*. Cambridge ; New York: Cambridge University Press. http://www.cs.ucl.ac.uk/staff/d.barber/brml/.

Bishop, Christopher M. 2006. *Pattern Recognition and Machine Learning*. Information Science and Statistics. New York: Springer.

Charniak, Eugene. 1991. â€śBayesian Networks Without Tears.â€ť *AI Magazine* 12 (4): 50.

Dawid, A. Philip. 1979. â€śConditional Independence in Statistical Theory.â€ť *Journal of the Royal Statistical Society. Series B (Methodological)* 41 (1): 1â€“31. http://people.csail.mit.edu/tdanford/discovering-causal-graphs-papers/dawid-79.pdf.

â€”â€”â€”. 1980. â€śConditional Independence for Statistical Operations.â€ť *The Annals of Statistics* 8 (3): 598â€“617. https://doi.org/10.1214/aos/1176345011.

Jordan, Michael Irwin. 1999. *Learning in Graphical Models*. Cambridge, Mass.: MIT Press.

Koller, Daphne, and Nir Friedman. 2009. *Probabilistic Graphical Models : Principles and Techniques*. Cambridge, MA: MIT Press.

Lauritzen, Steffen L. 1996. *Graphical Models*. Clarendon Press.

Montanari, Andrea. 2011. â€śLecture Notes for Stat 375 Inference in Graphical Models.â€ť http://www.stanford.edu/~montanar/TEACHING/Stat375/handouts/notes_stat375_1.pdf.

Murphy, Kevin P. 2012. *Machine Learning: A Probabilistic Perspective*. 1 edition. Cambridge, MA: The MIT Press.

Pearl, Judea. 2008. *Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference*. Rev. 2. print., 12. [Dr.]. The Morgan Kaufmann Series in Representation and Reasoning. San Francisco, Calif: Kaufmann.

â€”â€”â€”. 2009. *Causality: Models, Reasoning and Inference*. Cambridge University Press.

Pearl, Judea, Dan Geiger, and Thomas Verma. 1989. â€śConditional Independence and Its Representations.â€ť *Kybernetika* 25 (7): 33â€“44. http://dml.cz/bitstream/handle/10338.dmlcz/125413/Kybernetika_25-1989-7_6.pdf.