Convergence rates of the blocked Gibbs sampler with random scan in the Wasserstein metric

DOI10.1080/17442508.2019.1623795zbMath1490.60008OpenAlexW2946959687MaRDI QIDQ5086481

Guosheng Yin, Neng-Yi Wang

Publication date: 5 July 2022

Published in: Stochastics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/17442508.2019.1623795

zbMATH Keywords

Wasserstein metricblocked Gibbs samplerrandom scanDobrushin condition

Mathematics Subject Classification ID

Computational methods for problems pertaining to probability theory (60-08) Convergence of probability measures (60B10)

Cites Work

• Convergence rate and concentration inequalities for Gibbs sampling in high dimension
• Subgeometric rates of convergence in Wasserstein distance for Markov chains
• Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
• Bayesian computation and stochastic systems. With comments and reply.
• Measure concentration for Euclidean distance in the case of dependent random variables.
• Convergence rates of the random scan Gibbs sampler under the Dobrushin's uniqueness condition
• Convergence in the Wasserstein Metric for Markov Chain Monte Carlo Algorithms with Applications to Image Restoration
• A note on Markov chain Monte Carlo sweep strategies
• Blocking strategies and stability of particle Gibbs samplers
• Prescribing a System of Random Variables by Conditional Distributions
• Combinatorial criteria for uniqueness of Gibbs measures
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