Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling

DOI10.1007/s00180-017-0759-6zbMath1417.62051OpenAlexW2745833179MaRDI QIDQ722742

Shin-ichi Mayekawa, Kensuke Okada

Publication date: 27 July 2018

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-017-0759-6

zbMATH Keywords

MDSlatent variable modelingMCMC estimationoffline post-processing

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40) Applications of statistics to psychology (62P15)

Uses Software

• R
• WinBUGS
• smacof
• BUGS
• Mcmcpack
• JAGS
• Stan
• BayesDA
• Label.switching
• bmds
• NUTS

Cites Work

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• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
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• Using generalized Procrustes analysis for multiple imputation in principal component analysis
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• Bayesian hypothesis testing in latent variable models
• Joint Procrustes analysis for simultaneous nonsingular transformation of component score and loading matrices
• Generalized Procrustes analysis
• Maximum likelihood estimation in multidimensional scaling
• Orthogonal Procrustes rotation for two or more matrices
• Generalized joint Procrustes analysis
• Modern multidimensional scaling. Theory and applications.
• Multidimensional scaling. I: Theory and method
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Handbook of Markov Chain Monte Carlo
• Bayesian Multidimensional Scaling and Choice of Dimension
• Dealing With Label Switching in Mixture Models

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