Abstract
Summary In a Bayesian analysis of finite mixture models, parameter estimation and clustering are sometimes less straightforward than might be expected. In particular, the common practice of estimating parameters by their posterior mean, and summarizing joint posterior distributions by marginal distributions, often leads to nonsensical answers. This is due to the so-called ‘label switching’ problem, which is caused by symmetry in the likelihood of the model parameters. A frequent response to this problem is to remove the symmetry by using artificial identifiability constraints. We demonstrate that this fails in general to solve the problem, and we describe an alternative class of approaches, relabelling algorithms, which arise from attempting to minimize the posterior expected loss under a class of loss functions. We describe in detail one particularly simple and general relabelling algorithm and illustrate its success in dealing with the label switching problem on two examples.
Keywords
IdentifiabilityComputer scienceBayesian probabilityClass (philosophy)Posterior probabilityCluster analysisSimple (philosophy)Mixture modelMathematical optimizationEstimation theoryAlgorithmApplied mathematicsArtificial intelligenceMathematicsMachine learning
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Publication Info
- Year
- 2000
- Type
- article
- Volume
- 62
- Issue
- 4
- Pages
- 795-809
- Citations
- 1059
- Access
- Closed
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Cite This
Matthew Stephens
(2000).
Dealing With Label Switching in Mixture Models.
Journal of the Royal Statistical Society Series B (Statistical Methodology)
, 62
(4)
, 795-809.
https://doi.org/10.1111/1467-9868.00265
Identifiers
- DOI
- 10.1111/1467-9868.00265