Sciweavers

Share
SAC
2010
ACM

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

8 years 10 months ago
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models
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 o...
M. Sperrin, Thomas Jaki, E. Wit
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where SAC
Authors M. Sperrin, Thomas Jaki, E. Wit
Comments (0)
books