## AbstractThe label switching problem, the unidentifiability of the per- mutation of clusters or more generally latent variables, makes interpre- tation of results computed with MCMC sampling difficult. We introduce a fully Bayesian treatment of the permutations which performs better than alternatives. The method can even be used to compute summaries of the posterior samples for nonparametric Bayesian methods, for which no good solutions exist so far. Although being approximative in that case, the results are very promising. The summaries are intuitively ap- pealing: A summarized cluster is defined as a set of points for which the likelihood of being in the same cluster is maximized.
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