We present a novel MCMC sampler for Dirichlet process mixture models that can be used for conjugate or non-conjugate prior distributions. The proposed sampler can be massively parallelized to achieve significant computational gains. A non-ergodic restricted Gibbs iteration is mixed with split/merge proposals to produce a valid sampler. Each regular cluster is augmented with two sub-clusters to construct likely split moves. Unlike many previous parallel samplers, the proposed sampler accurately enforces the correct stationary distribution of the Markov chain without the need for approximate models. Empirical results illustrate that the new sampler exhibits better convergence properties than current methods.

We develop a sampling technique for Hierarchical Dirichlet process models. The parallel algorithm builds upon [Chang & Fisher 2013] by proposing large split and merge moves based on learned sub-clusters. The additional global split and merge moves drastically improve convergence in the experimental results. Furthermore, we discover that cross-validation techniques do not adequately determine convergence, and that previous sampling methods converge slower than were previously expected.

We present a novel MCMC sampler for Dirichlet process mixture models that can be used for conjugate or non-conjugate prior distributions. The proposed sampler can be massively parallelized to achieve significant computational gains. A non-ergodic restricted Gibbs iteration is mixed with split/merge proposals to produce a valid sampler. Each regular cluster is augmented with two sub-clusters to construct likely split moves. Unlike many previous parallel samplers, the proposed sampler accurately enforces the correct stationary distribution of the Markov chain without the need for approximate models.

We develop a sampling technique for Hierarchical Dirichlet process models. The parallel algorithm builds upon [Chang & Fisher 2013] by proposing large split and merge moves based on learned sub-clusters. The additional global split and merge moves drastically improve convergence in the experimental results. Furthermore, we discover that cross-validation techniques do not adequately determine convergence, and that previous sampling methods converge slower than were previously expected. Papers published at the Neural Information Processing Systems Conference.