Guest editors' introduction to the special issue on bayesian nonparametrics

The articles in this special issue discuss the applications supported by Bayesian nonparametric modeling. These probabilistic models defined over infinite-dimensional parameter spaces. For Gaussian process models of regression and classification functions, the parameter space consists of a set of continuous functions. For the Dirichlet process mixture models used in density estimation and clustering, the parameter space is dense in the space of probability measures. Bayesian nonparametric models provide a flexible framework for modeling complex data and a promising alternative to classical model selection methods. Due to recent computational advances, these approaches have received increasing attention in machine learning, statistics, probability, and related application domains.