Slow convergence of the Gibbs sampler

DOI10.2307/3315722zbMath0922.60058OpenAlexW2107836873MaRDI QIDQ4243790

Claude Bélisle

Publication date: 24 May 1999

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315722

zbMATH Keywords

Gibbs samplergeometric ergodicityhit-and-run samplerMarkov-chain Monte Carlo methods

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05)

Related Items (3)

Sampling hyperparameters in hierarchical models: Improving on Gibbs for high-dimensional latent fields and large datasets ⋮ Slow hit-and-run sampling ⋮ On convergence rates of Gibbs samplers for uniform distributions

Cites Work

• On convergence rates of Gibbs samplers for uniform distributions
• General hit-and-run Monte Carlo sampling for evaluating multidimensional integrals
• Markov chains for exploring posterior distributions. (With discussion)
• On the convergence of the Markov chain simulation method
• Rates of convergence of the Hastings and Metropolis algorithms
• Improving hit-and-run for global optimization
• Hit-and-run algorithms for the identification of nonredundant linear inequalities
• General Irreducible Markov Chains and Non-Negative Operators
• Sampling-Based Approaches to Calculating Marginal Densities
• Convergence properties of hit–and–run samplers
• Asymptotic Behavior of the Gibbs Sampler
• Hit-and-Run Algorithms for Generating Multivariate Distributions
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