MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models
Carl Edward Rasmussen and Dilan Gorur
In: ICML Workshop on Learning with Nonparametric Bayesian Methods, 29 Jun 2006, Pittsburgh, USA.

Abstract

We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.

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