Tutorials
- A Brief Introduction to Graphical Models and Bayesian Networks. Kevin Murphy, University of British Columbia, 1998.
- A Short Course on Graphical Models. Mark Paskin, Intel Berkeley Research Center, Summer 2003.
- Graphical models and variational methods: Message-passing and relaxations. Martin Wainwright, ICML 2008.
- Probabilistic Models for Unsupervised Learning. Zoubin Ghahramani and Sam Roweis, NIPS 1999.
- Nonparametric Bayesian Methods. Zoubin Ghahramani, UAI 2005.
- Dirichlet Processes, Chinese Restaurant Processes, and All That. Michael Jordan, NIPS 2005.
- An Introduction to Bayesian Nonparametric Modelling. Yee Whye Teh, 2009.
Courses
- Statistical Learning Theory: Graphical Models. UC Berkeley EECS 281a, Martin Wainwright, Fall 2008.
- Structured Probabilistic Models: Principles and Techniques. Stanford University CS 228, Daphne Koller, Winter 2010.
- Algorithms for Inference. MIT 6.438, William Freeman and Gregory Wornell, Fall 2009.
- Foundations of Probabilistic Modeling. Princeton COS513, David Blei, Spring 2009.
- Advanced Topics in AI: Probabilistic Graphical Models. University of Pennsylvania CIS 620, Ben Taskar, Spring 2009.
- Probabilistic and Unsupervised Learning. Gatsby Unit, Yee Whye Teh and Maneesh Sahani, 2009.
- Bayesian Machine Learning: Graphical Models and Approximate Inference. MPI, Matthias Seeger, Summer 2009.
- Statistical Computing: Monte Carlo Methods. University of British Columbia CPSC 535d, Arnaud Doucet, 2007.
Textbooks
- Probabilistic Graphical Models: Principles and Techniques. D. Koller & N. Friedman, MIT Press, 2009.
- Pattern Recognition and Machine Learning. C. Bishop, Springer, 2007.
- Information Theory, Inference, and Learning Algorithms. D. MacKay, Cambridge University Press, 2003.
- Probabilistic Networks and Expert Systems. R. Cowell, P. Dawid, S. Lauritzen, & D. Spiegelhalter, Springer, 2002.
- Probabilistic Reasoning in Intelligent Systems. J. Pearl, Elsevier, 1988.
- Causality. J. Pearl, Cambridge University Press, 2009.
- Graphical Models. S. Lauritzen, Oxford Statistical Science Series, 1996.
- Bayesian Data Analysis. A. Gelman, J. Carlin, H. Stern, & D. Rubin, CRC Press, 2003.
- Monte Carlo Statistical Methods. C. Robert & G. Casella, Springer, 2004.
- Sequential Monte Carlo Methods in Practice. A. Doucet, N. de Freitas, & N. Gordon, editors, Springer, 2001.
- The Elements of Statistical Learning. T. Hastie, R. Tibshirani, & J. Friedman, Springer, 2009.
- Convex Optimization. S. Boyd & L. Vandenberghe, Cambridge University Press, 2004.
Software
- Matlab Graphical Model Toolboxes. Kevin Murphy, University of British Columbia.
- Bayesian Modeling and Monte Carlo Methods. Radford Neal, University of Toronto.
- PNL: Probabilistic Networks Library.
- BUGS: Bayesian Inference using Gibbs Sampling.
- VIBES: Variational Inference for Bayesian Networks. John Winn, Microsoft Research.
- Infer.NET. Tom Minka and John Winn, Microsoft Research.
- Lightspeed Matlab Toolbox. Tom Minka, Microsoft Research.
- libDAI: Discrete Approximate Inference in Graphical Models. Joris Mooij, MPI.
- C_INFERENCE: Inference for Undirected Graphical Models. Talya Meltzer, Hebrew University.
- Gaussian Belief Propagation. Danny Bickson, CMU.
- ReBEL: Recursive Bayesian Estimation Library.