Results 1 
4 of
4
Dealing with label switching in mixture models
 Journal of the Royal Statistical Society, Series B
, 2000
"... In a Bayesian analysis of finite mixture models, parameter estimation and clustering are sometimes less straightforward that might be expected. In particular, the common practice of estimating parameters by their posterior mean, and summarising joint posterior distributions by marginal distributions ..."
Abstract

Cited by 112 (0 self)
 Add to MetaCart
In a Bayesian analysis of finite mixture models, parameter estimation and clustering are sometimes less straightforward that might be expected. In particular, the common practice of estimating parameters by their posterior mean, and summarising joint posterior distributions by marginal distributions, often leads to nonsensical answers. This is due to the socalled “labelswitching” problem, which is caused by symmetry in the likelihood of the model parameters. A frequent response to this problem is to remove the symmetry using artificial identifiability constraints. We demonstrate that this fails in general to solve the problem, and describe an alternative class of approaches, relabelling algorithms, which arise from attempting to minimise the posterior expected loss under a class of loss functions. We describe in detail one particularly simple and general relabelling algorithm, and illustrate its success in dealing with the labelswitching problem on two examples.
Bayesian methods for hidden markov models
 Journal of the American Statistical Association
"... ..."
Dealing with Multimodal Posteriors and NonIdentifiability in Mixture Models
, 1999
"... In a Bayesian analysis of finite mixture models, the lack of identifiability of the parameters often leads to a posterior distribution which is highly multimodal and symmetric, making it difficult to interpret or summarize. A common approach to this problem is to make the parameters identifiable by ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
In a Bayesian analysis of finite mixture models, the lack of identifiability of the parameters often leads to a posterior distribution which is highly multimodal and symmetric, making it difficult to interpret or summarize. A common approach to this problem is to make the parameters identifiable by imposing artificial constraints. We demonstrate that this may fail to solve the problem, and describe and illustrate an alternative solution which involves postprocessing the results of a Markov Chain Monte Carlo (MCMC) scheme. Our method can be viewed either as a method of searching for a reasonable summary of the posterior distribution, or as a method of revising the prior distribution.
Label Switch in Mixture Model and Relabeling Algorithm Project for Reading Course Prepared by: Fanfu Xie, ZhengFei ChenLabel Switch in Mixture Model and Relabeling Algorithm
"... When MCMC is used to perform Bayesian analysis for mixture models, the socall label switch problem affects the clustering analysis. If the problem is not handled properly, the ergodic average of the MCMC samples is not appropriate for the estimation of the parameters. In this paper, we will review t ..."
Abstract
 Add to MetaCart
When MCMC is used to perform Bayesian analysis for mixture models, the socall label switch problem affects the clustering analysis. If the problem is not handled properly, the ergodic average of the MCMC samples is not appropriate for the estimation of the parameters. In this paper, we will review the Label Switch problem in mixture model, discuss and implement the relabelling algorithm suggested by Stephens. To illustrate the problem, we apply data augmentation Gaussian Mixture Model to Galaxy data with different number of components.