Open Access
Translator Disclaimer
August 2013 MCMC for Normalized Random Measure Mixture Models
Stefano Favaro, Yee Whye Teh
Statist. Sci. 28(3): 335-359 (August 2013). DOI: 10.1214/13-STS422


This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Monte Carlo methods of both marginal type and conditional type. The proposed marginal samplers are generalizations of Neal’s well-regarded Algorithm 8 for Dirichlet process mixture models, whereas the conditional sampler is a variation of those recently introduced in the literature. For both the marginal and conditional methods, we consider as a running example a mixture model with an underlying normalized generalized Gamma process prior, and describe comparative simulation results demonstrating the efficacies of the proposed methods.


Download Citation

Stefano Favaro. Yee Whye Teh. "MCMC for Normalized Random Measure Mixture Models." Statist. Sci. 28 (3) 335 - 359, August 2013.


Published: August 2013
First available in Project Euclid: 28 August 2013

zbMATH: 1331.62138
MathSciNet: MR3135536
Digital Object Identifier: 10.1214/13-STS422

Keywords: Algorithm 8 , Bayesian nonparametrics , completely random measure , conditional sampler , Dirichlet process , hierarchical mixture model , marginalized sampler , MCMC posterior sampling method , normalized generalized gamma process , normalized random measure , slice sampling

Rights: Copyright © 2013 Institute of Mathematical Statistics


Vol.28 • No. 3 • August 2013
Back to Top