Dartmouth College; Carnegie Mellon University
Poster: Slice sampling normalized kernel-weighted completely random measure mixture models
7:00pm - 12:00am Monday, December 03, 2012
Harrah’s Special Events Center 2nd Floor
This is part of the Poster Session and Reception which begins at 19:00 on Monday December 3, 2012
A number of dependent nonparametric processes have been proposed to model non-stationary data with unknown latent dimensionality. However, the inference algorithms are often slow and unwieldy, and are in general highly specific to a given model formulation. In this paper, we describe a wide class of nonparametric processes, including several existing models, and present a slice sampler that allows efficient inference across this class of models.