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.

To taxonomise:

path

Barber’s (Barb12) taxonomy of graphical models.

Tidbits

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.