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.
- directed graphical models, and
- undirected graphical models
- a supposed unifying formalism factor graphs
- chain graphs are cliques of undirected graphs with directed connections
- conditional random fields
- hierarchical models are special cases of directed graphical models.
- quantum graphical models because it all looks a bit different with noncommutative probability.
Pedagogically useful, although probably not industrial-grade, David Barber’s discrete graphical model code (Julia).
- Barber, D. (2012) Bayesian reasoning and machine learning. . Cambridge ; New York: Cambridge University Press
- Jordan, M. I., & Weiss, Y. (2002a) Graphical models: Probabilistic inference. The Handbook of Brain Theory and Neural Networks, 490–496.
- Jordan, M. I., & Weiss, Y. (2002b) Probabilistic inference in graphical models. Handbook of Neural Networks and Brain Theory.
- Lauritzen, S. L.(1996) Graphical Models. . Clarendon Press
- Neapolitan, R. E., & others. (2004) Learning bayesian networks. (Vol. 38). Prentice Hall Upper Saddle River
- Studený, M. (2005) Probabilistic conditional independence structures. . London: Springer
- 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.