The label switching problem, the unidentifiability of the permutation of clusters or more generally latent variables, makes interpretation 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 appealing: A summarized cluster is defined as a set of points for which the likelihood of being in the same cluster is maximized. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Puolamäki, K., & Kaski, S. (2009). Bayesian solutions to the label switching problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5772 LCNS, pp. 381–392). https://doi.org/10.1007/978-3-642-03915-7_33
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