A note on Markov chain Monte Carlo sweep strategies

DOI10.1080/0094965042000223671zbMath1065.65005OpenAlexW1983229576MaRDI QIDQ4665922

Richard A. Levine

Publication date: 11 April 2005

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/0094965042000223671

zbMATH Keywords

Gibbs samplerrandom scanrandom variate generationMetropolis samplerdeterministic scanHastings samplerrandom sweep strategy

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40) Random number generation in numerical analysis (65C10)

Related Items (5)

Maximum likelihood estimation of the Markov-switching GARCH model ⋮ Convergence rates of the blocked Gibbs sampler with random scan in the Wasserstein metric ⋮ Comment: On random scan Gibbs samplers ⋮ Adaptive Gibbs samplers and related MCMC methods ⋮ A hybrid scan Gibbs sampler for Bayesian models with latent variables

Cites Work

• Comparing sweep strategies for stochastic relaxation
• Ordering and improving the performance of Monte Carlo Markov chains.
• Bayesian computation and stochastic systems. With comments and reply.
• Coordinate selection rules for Gibbs sampling
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Miscellanea. Peskun's theorem and a modified discrete-state Gibbs sampler
• Monte Carlo sampling methods using Markov chains and their applications

This page was built for publication: A note on Markov chain Monte Carlo sweep strategies