label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs

Abstract

Label switching is a well-known and fundamental problem in Bayesian estimation of mixture or hidden Markov models. In case that the prior distribution of the model parameters is the same for all states, then both the likelihood and posterior distribution are invariant to permutations of the parameters. This property makes Markov chain Monte Carlo (MCMC) samples simulated from the posterior distribution non-identifiable. In this paper, the \pkg{label.switching} package is introduced. It contains one probabilistic and seven deterministic relabelling algorithms in order to post-process a given MCMC sample, provided by the user. Each method returns a set of permutations that can be used to reorder the MCMC output. Then, any parametric function of interest can be inferred using the reordered MCMC sample. A set of user-defined permutations is also accepted, allowing the researcher to benchmark new relabelling methods against the available ones

Accepted to Journal of Statistical Software

Cited by in corpus (10)

• Bayesian mixture of Plackett-Luce models for partially ranked data
• On the identifiability of Bayesian factor analytic models
• Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models
• BayesMallows: An R Package for the Bayesian Mallows Model
• Overfitting Bayesian Mixtures of Factor Analyzers with an Unknown Number of Components
• Identifying Brexit voting patterns in the British House of Commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects
• Mixtures of multivariate generalized linear models with overlapping clusters
• CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model
• Multinomial mixture for spatial data
• Bayesian Finite Mixture Models