Improving the convergence properties of the data augmentation algorithm with an application to Bayesian mixture modeling

DOI10.1214/11-STS365zbMath1246.60095arXiv0911.4546WikidataQ60521589 ScholiaQ60521589MaRDI QIDQ449838

J. Herrera, D. Rodríguez-Gómez

Publication date: 1 September 2012

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0911.4546

zbMATH Keywords

eigenvalueMonte Carlospectrumsandwich algorithmcompact operatorconvergence rateMarkov operatorpositive operatoroperator normreversible Markov chainlabel switching

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items

Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors, Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors, Efficient Bayesian estimation of the multivariate double chain Markov model, Trace class Markov chains for the normal-gamma Bayesian shrinkage model, Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors, Spectral analytic comparisons for data augmentation, A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants, Conjugate priors and posterior inference for the matrix Langevin distribution on the Stiefel manifold, Trace Class Markov Chains for Bayesian Inference with Generalized Double Pareto Shrinkage Priors, Improving efficiency of data augmentation algorithms using Peskun's theorem, Consistent estimation of the spectrum of trace class data augmentation algorithms

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