Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition

DOI10.1214/13-STS423zbMath1331.60151arXiv0903.0664OpenAlexW3102450852MaRDI QIDQ5965030

Alicia A. Johnson, Ronald C. Neath, Galin L. Jones

Publication date: 2 March 2016

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

Full work available at URL: https://arxiv.org/abs/0903.0664

zbMATH Keywords

Monte CarloMarkov chainconvergence rateGibbs samplergeometric ergodicitymetropolis-within-Gibbsrandom scanuniform ergodicity

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (15)

Convergence rates of two-component MCMC samplers ⋮ A Hybrid Gibbs Sampler for Edge-Preserving Tomographic Reconstruction with Uncertain View Angles ⋮ Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals ⋮ Assessing and Visualizing Simultaneous Simulation Error ⋮ Bayesian variable selection in a finite mixture of linear mixed-effects models ⋮ Neuronized Priors for Bayesian Sparse Linear Regression ⋮ Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data ⋮ Subsampling sequential Monte Carlo for static Bayesian models ⋮ MALA-within-Gibbs Samplers for High-Dimensional Distributions with Sparse Conditional Structure ⋮ Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models ⋮ Convergence of Conditional Metropolis-Hastings Samplers ⋮ Multivariate initial sequence estimators in Markov chain Monte Carlo ⋮ Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems ⋮ Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition ⋮ Plateau proposal distributions for adaptive component-wise multiple-try metropolis

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