Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling

Journal article

In the past ten years there has been a dramatic increase of interest in the Bayesian analysis of finite mixture models. This is primarily because of the emergence of Markov chain Monte Carlo (MCMC) methods. While MCMC provides a convenient way to draw inference from complicated statistical models, there are many, perhaps underappreciated, problems associated with the MCMC analysis of mixtures. The problems are mainly caused by the nonidentifiability of the components under symmetric priors, w...

Expand abstract

Authors: Jasra, A; Holmes, C; Stephens, D

• Publication status: Published
• Journal: STATISTICAL SCIENCE
• Volume: 20
• Issue: 1
• Pages: 50-67
• Publication date: 2005-02-01
• DOI: 10.1214/08834230500000016
• ISSN: 0883-4237
• Language: English
• Keywords: sensitivity analysis; Bayesian statistics; identifiability; MCMC; label switching; mixture modeling