Sperrin, Matthew and Jaki, Thomas and Wit, Ernst (2010) Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models. Statistics and Computing, 20 (3). pp. 357-366. ISSN 0960-3174
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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 existing approaches rely upon specifying a loss function, and relabelling by minimising its posterior expected loss. In this paper we develop probabilistic approaches to relabelling that allow estimation and incorporation of the uncertainty in the relabelling process. Variants of the probabilistic relabelling algorithm are introduced and compared to existing loss function based methods. We demonstrate that the idea of probabilistic relabelling can be expressed in a rigorous framework based on the EM algorithm.
|Journal or Publication Title:||Statistics and Computing|
|Uncontrolled Keywords:||Bayesian - Identifiability - Label switching - MCMC - Mixture model|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited By:||Mr Matthew Sperrin|
|Deposited On:||08 May 2009 13:21|
|Last Modified:||23 Jan 2017 01:30|
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- Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models. (deposited 03 Mar 2009 11:20)
- Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models. (deposited 08 May 2009 13:21)[Currently Displayed]
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