A nonparametric model is a model on an infinite dimensional parameter space. The parameter space represents the set of all possible solutions for a given learning problem -- for example, the set of smooth functions in nonlinear regression, or of all probability densities in a density estimation problem. A Bayesian nonparametric model defines a prior distribution on such an infinite dimensional space, where the traditional prior assumptions (e.g. "the parameter is likely to be small") are replaced by structural assumptions ("the function is likely to be smooth"), and learning then requires computation of the posterior distribution given data.

The tutorial will provide a high-level introduction to modern Bayesian nonparametrics. Since first attracting attention at NIPS a decade ago, the field has evolved substantially in the machine learning, statistics and probability communities. We now have a much improved understanding of how novel models can be used effectively in applications, of their theoretical properties, of techniques for posterior computation, and of how they can be combined to fit the requirements of a given problem. In the tutorial, we will survey the current state of the art with a focus on recent developments of interest in machine learning.