Mixture Model MCMC Inference

We develop parallelizable samplers for Dirichlet process mixture models that do not require approximating the infinite model. Two sub-clusters are fit for each regular-cluster, and are used to propose large split and merge moves. Inference is shown to be orders of magnitude faster than traditional Gibbs sampling while being more robust to different initializations and hyper-parameters.