Practical Machine Learning Lecture: Nonparametric Bayesian methods (Dirichlet processes)

Nonparametric Bayesian methods (Dirichlet processes)
Lecturer: Kurt Miller
Date: Nov 19

References

There are numerous references on Bayesian methods and Markov Chain Monte Carlo (MCMC) techniques. Three useful textbooks are:

Bayesian Data Analaysis. Gelman, Carlin, Stern, Rubin.
The Bayesian Choice. Robert.
Monte Carlo Statistical Methods. Robert, Casella.

Unfortunately, there currently are no good introductory textbooks on the Dirichlet Process. One of the best introductions to Dirichlet Processes is Chapter 2, Section 2.5 of Erik Sudderth's PhD thesis.

A great set of nonparametric Bayesian references can be found in the references section of a 2008 workshop on nonparametric Bayesian methods at ICML/UAI. This list includes many of the foundational papers about Dirichlet Processes as well as references to many recent applications.

Tutorials on nonparametric Bayesian methods

Dirichlet Process Tutorial and Practical Course - Yee Whye Teh, Machine Learning Summer School 2007.
Structured Bayesian nonparametric models with variational inference - Dan Klein and Percy Liang, ACL 2007.
Dirichlet processes, Chinese restaurant processes and all that - Michael Jordan, NIPS 2005.
Nonparametric Bayesian Learning - Zoubin Ghahramani, UAI 2005.