Markov chain Monte Carlo and Rao-Blackwellization

DOI10.1016/S0378-3758(99)00079-8zbMath0968.62059OpenAlexW2060574014MaRDI QIDQ1973294

Ian W. McKeague, Wolfgang Wefelmeyer

Publication date: 17 September 2001

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(99)00079-8

zbMATH Keywords

conditioningIsing modelGibbs samplerMetropolis algorithmdata augmentationempirical estimator

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Nonparametric estimation (62G05) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (4)

Rao–Blackwellisation in the Markov Chain Monte Carlo Era ⋮ Parallel hierarchical sampling: a general-purpose interacting Markov chains Monte Carlo algorithm ⋮ Ordering and improving the performance of Monte Carlo Markov chains. ⋮ Information bounds for Gibbs samplers

Cites Work

• Markov chains and stochastic stability
• Behavior of droplets for a class of Glauber dynamics at very low temperature
• Optimal spectral structure of reversible stochastic matrices, Monte Carlo methods and the simulation of Markov random fields
• Outperforming the Gibbs sampler empirical estimator for nearest-neighbor random fields
• Markov chains for exploring posterior distributions. (With discussion)
• Sampling-Based Approaches to Calculating Marginal Densities
• The Calculation of Posterior Distributions by Data Augmentation
• Rao-Blackwellisation of sampling schemes
• Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
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