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.
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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