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# Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models

@article{Sperrin2010ProbabilisticRS, title={Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models}, author={Matthew Sperrin and Thomas Jaki and Ernst Wit}, journal={Statistics and Computing}, year={2010}, volume={20}, pages={357-366} }

- Published 2010 in Statistics and Computing
DOI:10.1007/s11222-009-9129-8

The label switching problem is caused by the likelihood of a Bayesian mixture model being invariant to permutations of the labels. The permutation can change multiple times between Markov Chain Monte Carlo (MCMC) iterations making it difficult to infer component-specific parameters of the model. Various so-called ‘relabelling’ strategies exist with the goal to ‘undo’ the label switches that have occurred to enable estimation of functions that depend on component-specific parameters. Most… CONTINUE READING

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