Fast Bayesian Inference in Dirichlet Process Mixture Models
By Lianming Wang, David B. Dunson. 2011
109 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistics and Probability
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Citations in the corpus
- A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning (2020, methodology): “Since the exact inference of the posterior of DPM models is infeasible, approximate inference methods are adopted such as Markov chain Monte Carlo (MCMC) (Maceachern, 1994; Escobar & West, 1995; Neal, 2000) or variational inference (Blei & Jordan, 2006; Wang & Dunson, 2011; Lin, 2013).”
- Fast unsupervised ego-action learning for first-person sports videos (2011, methodology): “Typical inference algorithm include MCMC [5], variational inference [1], multi-pass online inference [15] and online beam search [2].”
- Fast hierarchical Bayesian analysis of population structure (2018, methodology): “Other approximate inference methods for DPM models include variational inference (21), fast search (22), sequential updating and greedy search (SUGS) (23,24) and expectation propagation (25).”