Convergence of Conditional Metropolis-Hastings Samplers

DOI10.1239/aap/1401369701zbMath1379.60082OpenAlexW2139831589MaRDI QIDQ5169501

Gareth O. Roberts, Jeffrey S. Rosenthal, Galin L. Jones

Publication date: 10 July 2014

Published in: Advances in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aap/1401369701

zbMATH Keywords

convergence rateGibbs samplergeometric ergodicityMarkov chain Monte Carlo algorithmindependence sampler

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Bayesian inference (62F15) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05)

Related Items

Geometric ergodicity of Gibbs samplers for the horseshoe and its regularized variants, Convergence rates of two-component MCMC samplers, Assessing and Visualizing Simultaneous Simulation Error, Geometric ergodicity of a more efficient conditional Metropolis-Hastings algorithm, Bayesian variable selection in a finite mixture of linear mixed-effects models, Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models, Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects, Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition, Improving efficiency of data augmentation algorithms using Peskun's theorem, A hybrid scan Gibbs sampler for Bayesian models with latent variables

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