Slice sampling normalized kernel-weighted completely random measure mixture models.

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.