Handling the Label Switching Problem in Latent Class Models Via the ECR Algorithm

DOI10.1080/03610918.2012.718840zbMath1291.62071OpenAlexW2070844275MaRDI QIDQ5415894

Panagiotis Papastamoulis

Publication date: 19 May 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.718840

zbMATH Keywords

hidden Markov modelslatent variablesmixtures of regressionsBayesian image segmentationbayesian analysisECR algorithmlabel switching phenomenon

Mathematics Subject Classification ID

Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (6)

Bayesian variable selection in a finite mixture of linear mixed-effects models ⋮ A data-driven reversible jump for estimating a finite mixture of regression models ⋮ Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components ⋮ On the estimation of mixtures of Poisson regression models with large number of components ⋮ Relabelling algorithms for mixture models with applications for large data sets ⋮ Detecting multiple random changepoints in Bayesian piecewise growth mixture models

Cites Work

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• On the convergence rate of random permutation sampler and ECR algorithm in missing data models
• Reversible jump and the label switching problem in hidden Markov models
• Calculating posterior distributions and modal estimates in Markov mixture models
• Bayesian estimation of hidden Markov chains: A stochastic implementation
• Inference in hidden Markov models.
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
• Hidden Markov Models and Disease Mapping
• Dealing With Label Switching in Mixture Models
• Bayesian Inference in Hidden Markov Models Through the Reversible Jump Markov Chain Monte Carlo Method
• Bayesian Variable Selection in Markov Mixture Models
• Fully Bayesian analysis of switching Gaussian state space models

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