The course will be based mostly on the following sources:
Two additional books:
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, SpringerVerlag
1997
 Robert G. Cowell, A. Philip Dawid,
Steffen L. Lauritzen, David J. Spiegelhalter Probabilistic Networks and Expert
Systems SpringerVerlag, 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,
ContextSpecific
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
TimeSliced Bayesian Networks.
 Pearl, Causation,
Action and Counterfactuals.
 Dechter,Minibuckets:
a general scheme for approximating inference.
Extended report.
 Darwiche,
Recursive
Conditioning: Anyspace conditioning algorithm with treewidthbounded
complexity.
 Darwiche,Anyspace
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. Spaceefficient
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 TimeSpace Tradeoff
 Gagliardi,
F., Generalizing
Variable Elimination In Bayesian Networks
 Rish, I. and Dechter, R., AAAI
2000 Tutorial
 Other related links:
