Parallelizing MCMC for Bayesian nonparametrics
Ph.D Sinead Williamson
(University of Texas at Austin)
models, such as those based on the Dirichlet process and the Pitman-Yor
process, provide elegant and flexible alternatives to parametric models
when the number of underlying components is unknown or growing.
Unfortunately, inference in such models can be slow, and previous
parallelization methods have relied on introducing approximations which
can lead to inaccuracies in the posterior estimate. In this talk, I
will construct auxiliary variable representations for the Dirichlet
process, the Pitman-Yor process, and some hierarchical extensions, and
show how these representations facilitate the development of
distributed Markov chain Monte Carlo schemes that use the correct
equilibrium distribution. Experimental analyses show that this approach
allows scalable inference without the deterioration in estimate quality
that accompanies existing methods.
Joint work with Avinava Dubey and Eric Xing.
Fecha : Miercoles 29 de Octubre
Lugar : Auditorio IIMAS
Hora : 12:00 horas