title: Fast Bayesian Inference in Dirichlet Process Mixture Models.
abstract: There has been increasing interest in applying Bayesian nonparametric methods in large samples and high dimensions. As Markov chain Monte Carlo (MCMC) algorithms are often infeasible, there is a pressing need for much faster algorithms. This article proposes a fast approach for inference in Dirichlet process mixture (DPM) models. Viewing the partitioning of subjects into clusters as a model selection problem, we propose a sequential greedy search algorithm for selecting the partition. Then, when conjugate priors are chosen, the resulting posterior conditionally on the selected partition is available in closed form. This approach allows testing of parametric models versus nonparametric alternatives based on Bayes factors. We evaluate the approach using simulation studies and compare it with four other fast nonparametric methods in the literature. We apply the proposed approach to three datasets including one from a large epidemiologic study. Matlab codes for the simulation and data analyses using the proposed approach are available online in the supplemental materials.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, SC 29208.
authors.forename: Lianming
authors.initials: L
authors.lastname: Wang
authors.forename: David B
authors.initials: DB
authors.lastname: Dunson
keywords: Clustering; Density estimation; Efficient computation; Large samples; Nonparametric Bayes; Pólya urn scheme; Sequential analysis