Slow hit-and-run sampling

DOI10.1016/S0167-7152(99)00134-0zbMath0954.60053OpenAlexW2068828332WikidataQ127594991 ScholiaQ127594991MaRDI QIDQ1975350

Claude Bélisle

Publication date: 5 February 2001

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(99)00134-0

zbMATH Keywords

Markov kernelsmall perturbationstarget distributionhit-and-run sampler

Mathematics Subject Classification ID

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

Related Items (1)

Exploiting sparsity for semi-algebraic set volume computation

Cites Work

• On convergence rates of Gibbs samplers for uniform distributions
• On the convergence of the Markov chain simulation method
• Hit-and-run mixes fast
• Hit-and-run algorithms for the identification of nonredundant linear inequalities
• Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Convergence properties of hit–and–run samplers
• Slow convergence of the Gibbs sampler
• Convergence Properties of Perturbed Markov Chains
• Hit-and-Run Algorithms for Generating Multivariate Distributions

This page was built for publication: Slow hit-and-run sampling