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.
- Barber, D. (2012) Bayesian reasoning and machine learning. . Cambridge ; New York: Cambridge University Press
- Bishop, C. M.(2006) Pattern recognition and machine learning. . New York: Springer
- Dawid, A. P.(1979) Conditional independence in statistical theory. Journal of the Royal Statistical Society. Series B (Methodological), 41(1), 1–31.
- Dawid, A. P.(1980) Conditional Independence for Statistical Operations. The Annals of Statistics, 8(3), 598–617. DOI.
- Jordan, M. I.(1999) Learning in graphical models. . Cambridge, Mass.: MIT Press
- Koller, D., & Friedman, N. (2009) Probabilistic graphical models : principles and techniques. . Cambridge, MA: MIT Press
- Lauritzen, S. L.(1996) Graphical Models. . Clarendon Press
- Pearl, J. (2008) Probabilistic reasoning in intelligent systems: networks of plausible inference. (Rev. 2. print., 12. [Dr.].). San Francisco, Calif: Kaufmann
- Pearl, J. (2009) Causality: Models, Reasoning and Inference. . Cambridge University Press
- Pearl, J., Geiger, D., & Verma, T. (1989) Conditional independence and its representations. Kybernetika, 25(7), 33–44.