Computational Statistics & Data Analysis
Bayesian density estimation using skew student-t-normal mixtures
Computational Statistics & Data Analysis
Bayesian Solutions to the Label Switching Problem
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models
Statistics and Computing
A Bayesian approach to model-based clustering for binary panel probit models
Computational Statistics & Data Analysis
Model based labeling for mixture models
Statistics and Computing
On convergence rates of mixtures of polynomial experts
Neural Computation
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The mixture model likelihood function is invariant with respect to permutation of the components of the mixture. If functions of interest are permutation sensitive, as in classification applications, then interpretation of the likelihood function requires valid inequality constraints and a very large sample may be required to resolve ambiguities. If functions of interest are permutation invariant, as in prediction applications, then there are no such problems of interpretation. Contrary to assessments in some recent publications, simple and widely used Markov chain Monte Carlo (MCMC) algorithms with data augmentation reliably recover the entire posterior distribution.