Having studied parametric Bayesian statistics during the two last years, I plan to begin to self-study non parametric Bayesian model during this summer and look for recommendations. I would like the book(s) to cover both the theoritical aspects and the practical ones (with implementation, examples of models ...) Thanks for your recommendations.
Part V of Bayesian Data Analysis is on non-linear and non-parametric methods, which as I recall has chapters on each of basis function methods, Gaussian processes, and Dirichlet processes. (Don't have my copy handy.)
Gaussian Processes for Machine Learning is comprehensive, covering both theory and implementation, and is freely available online.
If you're interested in resources outside of full books, the tutorials by Chris Fonnesbeck on Dirichlet and Gaussian processes were very valuable to me. (Sections 5.1 and 5.2 in the "Notebooks" folder.)
Last, the Machine Learning Summer School 2009 lectures include two talks on non-parametric Bayesian methods. I haven't seen those two yet, but every other lecture I've watched in the series gave a top-notch introduction to its topic.
Here is a good collection to buy. I like the "Bundle of algorithms in Java", it gives straight out implementations/examples as does "Machine learning, practical tools and techniques" which is also a great book with practical examples. Hope that helps
A fantastic reference is Fundamentals of Nonparametric Bayesian Inference by Ghosal and van der Vaart.