1) Jordan, Bishop, Introduction to graphical models - available on-line (Need Password).
2) Bishop, Neural networks for pattern recognition
3) Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
4) Lauritzen, Graphical Models
5) Jordan (ed.), Learning in Graphical Models
6) Jordan (ed.), Graphical Models. Foundation of Neural Computation.
7) Yedidia, Freeman, Weiss, Generalized Belief Propagation
8) Alj, McEliece, The Generalized Distributive Law
9) Alj, McEliece, The Generalized Distributive Law and Free Energy Minimization
10) Weiss, Comparing the Mean Field Method and Belief Propagation for approximate inference in MRF’s
11) Murphy, Weiss, Jordan, Loopy Belief Propagation for Approximate Inference: An Empirical Study
12) Weiss, Correctness of Belief Propagation in Gaussian graphical models of arbitrary topology.
13) Yedidia, Freeman, Weiss, Bethe Free Energy, Kikuchi apoproximation and belief propagation algorithms
14) Weiss, Freeman, On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs
15) Socher, Sagerer, Perona, Bayesian Reasoning on Qualitative Descriptions from Image and Speech
16) Freeman, Pasztor, Carmichael, Learning Low Level Vision
17) Welling, Labelling with Loopy Belief Revision (personal communication).
18) Song, Goncalves, Perona, Unsupervised Learning of Human Motion Models
1) IDIS Labs - Bayesian Networks Tutorial.htm
2) A Brief Introduction to Graphical Models and Bayesian Networks
3) Michael I. Jordan's Home Page