Convergence rates of symmetric scan Gibbs sampler

DOI10.1007/s11464-019-0791-7zbMath1427.60043OpenAlexW2979090785WikidataQ127216496 ScholiaQ127216496MaRDI QIDQ2279101

Neng-Yi Wang

Publication date: 12 December 2019

Published in: Frontiers of Mathematics in China (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11464-019-0791-7

zbMATH Keywords

Wasserstein metricMarkov chain Monte Carlocoupling methodsymmetric scan Gibbs sampler

Mathematics Subject Classification ID

Inequalities; stochastic orderings (60E15) Monte Carlo methods (65C05)

Cites Work

• Convergence rate and concentration inequalities for Gibbs sampling in high dimension
• Subgeometric rates of convergence in Wasserstein distance for Markov chains
• Curvature, concentration and error estimates for Markov chain Monte Carlo
• Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
• Poincaré and transportation inequalities for Gibbs measures under the Dobrushin uniqueness condition
• General state space Markov chains and MCMC algorithms
• Bayesian computation and stochastic systems. With comments and reply.
• Measure concentration for Euclidean distance in the case of dependent random variables.
• Transportation cost-information inequalities and applications to random dynamical systems and diffusions.
• Convergence in the Wasserstein Metric for Markov Chain Monte Carlo Algorithms with Applications to Image Restoration
• Prescribing a System of Random Variables by Conditional Distributions
• Dobrushin Conditions and Systematic Scan
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