Both describe graphical models in general, specific models which we provide as Infer.NET examples, and the approximate inference algorithms available in Infer.NET.

Probabilistic models

• Kevin Murphy has a good introduction to probabilistic models and Bayesian networks: http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html

• The chapters on Uncertainty and Probablistic Modelling (13 to 15) in Russel and Norvig's standard AI text (3rd edition) have been recommended on the forum.

Approximate Inference

• Chapters 16 (Message Passing) and 33 (Variational Methods) of David Mackay's textbook (Information Theory, Inference, and Learning Algorithms: http://www.inference.phy.cam.ac.uk/mackay/itila/book.html) are relevant.
• The key reference for Expectation Propagation is Tom Minka's paper http://research.microsoft.com/en-us/um/people/minka/papers/ep/minka-ep-uai.pdf and for Variational Message Passing is John Winn's paper http://johnwinn.org/Publications/papers/VMP2004.pdf
• Tom also maintains a website with links to papers on EP: http://research.microsoft.com/en-us/um/people/minka/papers/ep/roadmap.html