Bayesian solutions to the label switching problem

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Journal Title
Journal ISSN
Volume Title
Faculty of Information and Natural Sciences | D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys
Date
2008
Major/Subject
Mcode
Degree programme
Language
en
Pages
iii, 8
Series
TKK reports in information and computer science, 7
Abstract
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 be used to compute summaries of the posterior samples even for nonparametric Bayesian methods, for which no good solutions exist so far. Although being approximative in this 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.
Description
Keywords
label switching, mixture models
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Citation
Permanent link to this item
https://urn.fi/urn:nbn:fi:tkk-011496