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label.switching

Relabelling MCMC Outputs of Mixture Models

The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.

Functions (14)

aic

Artificial Identifiability Constraints

compare.clust

Make all estimated clusters agree with a pivot allocation

dataBased

Data-based labelling

data_list

Simulated MCMC sample and related information

ecr

ECR algorithm (default version)

ecr.iterative.1

ECR algorithm (iterative version 1)

ecr.iterative.2

ECR algorithm (iterative version 2)

label.switching

Main calling function

label.switching-package

Algorithms for solving the label switching problem

lamb

Fetal lamb dataset

permute.mcmc

Reorder MCMC samples

pra

PRA algorithm

sjw

Probabilistic relabelling algorithm

stephens

Stephens' algorithm

label.switching

Relabelling MCMC Outputs of Mixture Models

v1.8
GPL-2
Authors
Panagiotis Papastamoulis
Initial release
2019-07-01

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