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COMPSTAT pp 227–232Cite as

Bayesian Inference for Mixture: The Label Switching Problem

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Abstract

A K-component mixture distribution is invariant to permutations of the labels of the components. As a consequence, in a Bayesian framework, the posterior distribution of the mixture parameters has theoretically K! modes. This fact involves possible difficulties when interpreting this posterior distribution. In this paper, we discuss the problem of labelling and we propose a simple and general clustering-like tool to deal with this problem.

Keywords

  • MCMC algorithm
  • labelling latent structure
  • k-means algorithm

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  • DOI: 10.1007/978-3-662-01131-7_26
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© 1998 Springer-Verlag Berlin Heidelberg

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Celeux, G. (1998). Bayesian Inference for Mixture: The Label Switching Problem. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_26

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  • DOI: https://doi.org/10.1007/978-3-662-01131-7_26

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1131-5

  • Online ISBN: 978-3-662-01131-7

  • eBook Packages: Springer Book Archive