Abstract:

Recently a number of Bayesian nonparametric methods have been developed for multivariate density estimation. These methods have attractive theoretical properties and are also computationally tractable. In this talk we will review this development with particular attention on the use of the density estimate as a building block to design methods for more complex machine learning tasks. These will be illustrated by applications in image analysis and flow cytometry.