Tutorial on Nonparametric Bayesian Methods

Zoubin Ghahramani
Bayesian methods provide a sound statistical framework for modelling and decision making. However, most simple parametric models are not realistic for modelling real-world data. Non-parametric models are much more flexible and therefore are much more likely to capture our beliefs about the data. They also often result in much better predictive performance. I will give a survey/tutorial of the field of non-parametric Bayesian statistics from the perspective of machine learning. Topics will include:
* The need for non-parametric models
* Gaussian processes and their application to classification, regression, and other prediction problems
* Chinese restaurant processes, different constructions, Pitman-Yor processes
* Dirichlet processes, Dirichlet process mixtures, Hierarchical Dirichlet processes and infinite HMMs
* Polya trees
* Dirichlet diffusion trees
* Time permitting, some new work on Indian buffet processes

The slides.