The course will be based mostly on the following sources:

• Rina Dechter, "Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms"
• Adnan Darwiche, "Modeling and Reasoning with Bayesian Networks"
• Rina Dechter, "Constraint Processing"
• Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference"
• Class notes (downloads linked in schedule on main page)

Two additional books:

• Daphne Koller and Nier Friedman, Probabilistic Graphical Models: Principles and Techniques. The MIT Press, 2009
• Richard E. Neapolitan, Learning Bayesian Networks. Prentice Hall, 2003.

The following is a (partial) list of secondary reading material:

• Books:
• K.B. Korb and A. E. Nicholson, Bayesian Artificial Intelligence. Chapman and Hall/CRC, 2004
• Finn V. Jensen. An introduction to Bayesian networks. UCL Press, 1996.
• Russell and Norvig, Artificial intelligence, a modern approach (chapters, 13,14,15)
• J. Pearl, Causality, Models, Reasoning and Inference, Cambridge University Press, 2000
• Castillo, E.; Gutierrez, J.M.; Hadi, A.S., Expert Systems and Probabilistic Network Models, Springer-Verlag 1997
• Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, David J. Spiegelhalter Probabilistic Networks and Expert Systems Springer-Verlag, 1999.
• Articles:
• Daphne Koller, Nir Friedman, Lise Getoor and Ben Taskar, Graphical Models in a Nutshell.
• Heckerman and Breese, Causal Independence for Probability Assessment and Inference Using Bayesian Networks.
• Boutilier, Friedman, Goldszmidt and Koller, Context-Specific Independence in Bayesian Networks.
• Dechter, Bucket Elimination: A Unifying Framework for Probabilistic Inference. 1998.
• Dechter, "AAAI98 tutorial on reasoning."
• Heckerman, A Tutorial on Learning with Bayesian Networks.
• Kjaerulff, dHugin: A Computational System for Dynamic Time-Sliced Bayesian Networks.
• Pearl, Causation, Action and Counterfactuals.
• Dechter,Mini-buckets: a general scheme for approximating inference. Extended report.
• Darwiche, Recursive Conditioning: Any-space conditioning algorithm with treewidth-bounded complexity.
• Darwiche,Any-space probabilistic inference.
• Darwiche,On the role of partial differentiation in probabilistic inference.
• Horvitz, Breese, Heckerman, Hovel & Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users.
• Binder, Murphy, Russell. Space-efficient inference in dynamic probabilistic networks.
• Russell, Binder, Koller, Kanazawa. Local learning in probabilistic networks with hidden variables.
• Dugad & Desai. A Tutorial on Hidden Markov Models.
• Friedman, Geiger, Goldszmidt. Bayesian Network Classifiers.
• Dechter, R., El Fattah, Y., Topological Parameters For Time-Space Tradeoff
• Gagliardi, F., Generalizing Variable Elimination In Bayesian Networks
• Rish, I. and Dechter, R., AAAI 2000 Tutorial
• Other related links:
• A Brief Introduction to Graphical Models and Bayesian Networks
• The Association for Uncertainty in Artificial Intelligence
  Sponsors the conference on Uncertainty in Artificial Intelligence (UAI), which is the main yearly forum for reporting research results relating to Bayesian networks.
• Free Bayesian Network Packages
• Artificial Intelligence: Probability studies may give computers a chance to learn, by Kevin J. Delaney in Cambridge, England. The Wall Street Journal Europe.
• AI on the web
• UAI repository