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's book Machine Learning: a Probabilistic Perspective gives excellent coverage of probabilistic Machine Learning with plenty of examples - it is targeted at undergraduates as well as graduates.
• Kevin Murphy also has a good tutorial paper that covers 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.
• Daphne Koller teaches an online course on Probabilistic Graphical Models: https://www.coursera.org/#course/pgm
• Sebastian Thrun and Peter Norvig gave an online course course "Introduction to Artificial Intelligence" which covers many of the Infer.NET concepts at: https://www.ai-class.com/

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