Geometric ergodicity of a more efficient conditional Metropolis-Hastings algorithm

DOI10.1080/03610926.2020.1719418OpenAlexW3003824790MaRDI QIDQ5079080

Alicia A. Johnson, Jianan Hui, James M. Flegal

Publication date: 25 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2020.1719418

zbMATH Keywords

Markov chain Monte Carlogeometric ergodicityMetropolis-Hastings

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• spBayes
• truncnorm

Cites Work

• Unnamed Item
• Unnamed Item
• Markov chain Monte Carlo estimation of quantiles
• Markov chains and stochastic stability
• Markov chain Monte Carlo: can we trust the third significant figure?
• On the Markov chain central limit theorem
• A note on Metropolis-Hastings kernels for general state spaces
• Geometric ergodicity and hybrid Markov chains
• Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
• A modified conditional Metropolis-Hastings sampler
• Nonasymptotic bounds on the estimation error of MCMC algorithms
• Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
• Fixed-Width Output Analysis for Markov Chain Monte Carlo
• Miscellanea. Peskun's theorem and a modified discrete-state Gibbs sampler
• Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow
• Convergence of Conditional Metropolis-Hastings Samplers
• Monte Carlo strategies in scientific computing

This page was built for publication: Geometric ergodicity of a more efficient conditional Metropolis-Hastings algorithm