Efficient Classification-Based Relabeling in Mixture Models.

Published

Journal Article

Effective component relabeling in Bayesian analyses of mixture models is critical to the routine use of mixtures in classification with analysis based on Markov chain Monte Carlo methods. The classification-based relabeling approach here is computationally attractive and statistically effective, and scales well with sample size and number of mixture components concordant with enabling routine analyses of increasingly large data sets. Building on the best of existing methods, practical relabeling aims to match data:component classification indicators in MCMC iterates with those of a defined reference mixture distribution. The method performs as well as or better than existing methods in small dimensional problems, while being practically superior in problems with larger data sets as the approach is scalable. We describe examples and computational benchmarks, and provide supporting code with efficient computational implementation of the algorithm that will be of use to others in practical applications of mixture models.

Full Text

Duke Authors

Cited Authors

  • Cron, AJ; West, M

Published Date

  • February 2011

Published In

Volume / Issue

  • 65 / 1

Start / End Page

  • 16 - 20

PubMed ID

  • 21660126

Pubmed Central ID

  • 21660126

Electronic International Standard Serial Number (EISSN)

  • 1537-2731

International Standard Serial Number (ISSN)

  • 0003-1305

Digital Object Identifier (DOI)

  • 10.1198/tast.2011.10170

Language

  • eng