Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Panagiotis Papastamoulis
Title: 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 label.switching package is introduced. It contains one probabilistic and seven deterministic relabeling 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 relabeling methods against the available ones.

Page views:: 3318. Submitted: 2013-10-13. Published: 2016-02-01.
Paper: label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs     Download PDF (Downloads: 1089)
label.switching_1.5.tar.gz: R source package Download (Downloads: 144; 55KB)
v69c01.R: R replication code Download (Downloads: 200; 34KB)

DOI: 10.18637/jss.v069.c01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.