Relabelling in Bayesian mixture models by pivotal units

@article{Egidi2018RelabellingIB,
  title={Relabelling in Bayesian mixture models by pivotal units},
  author={Leonardo Egidi and Roberta Pappad{\`a} and Francesco Pauli and Nicola Torelli},
  journal={Statistics and Computing},
  year={2018},
  volume={28},
  pages={957-969}
}
Label switching is a well-known and fundamental problem in Bayesian estimation of finite mixture models. It arises when exploring complex posterior distributions by Markov Chain Monte Carlo (MCMC) algorithms, because the likelihood of the model is invariant to the relabelling of mixture components. If the MCMC sampler randomly switches labels, then it is unsuitable for exploring the posterior distributions for component-related parameters. In this paper, a new procedure based on the post-MCMC… 
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