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Slice sampling normalized kernel-weighted completely random measure mixture models
Nick Foti · Sinead Williamson

Mon Dec 03 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None

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.

Author Information

Nick Foti (Apple & University of Washington)
Sinead Williamson (UT Austin)

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