Outperforming the Gibbs sampler empirical estimator for nearest-neighbor random fields

DOI10.1214/aos/1032298276zbMath0871.62083OpenAlexW2008243437MaRDI QIDQ1354360

Wolfgang Wefelmeyer, Ian W. McKeague, Prescilla E. Greenwood

Publication date: 26 May 1997

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1032298276

zbMATH Keywords

simulationMarkov chain Monte Carloasymptotic relative efficiencyasymptotic varianceIsing modelvariance reductionMetropolis-Hastings algorithmempirical estimatorsGibbs samplersMarkov chain sampling schemenearest-neighbor random fieldsparallel updating

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Asymptotic properties of nonparametric inference (62G20) Markov processes: estimation; hidden Markov models (62M05) Discrete-time Markov processes on general state spaces (60J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items

Ordering and improving the performance of Monte Carlo Markov chains. ⋮ Markov chain Monte Carlo and Rao-Blackwellization ⋮ Information bounds for Gibbs samplers

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Markov chains and stochastic stability
• Efficient estimation of the stationary distribution for exponentially ergodic Markov chains
• Evidential reasoning using stochastic simulation of causal models
• Comparing sweep strategies for stochastic relaxation
• Optimal spectral structure of reversible stochastic matrices, Monte Carlo methods and the simulation of Markov random fields
• On the rate of convergence of the Metropolis algorithm and Gibbs sampler by geometric bounds
• Bayesian analysis in expert systems. With comments and a rejoinder by the authors
• Markov chains for exploring posterior distributions. (With discussion)
• On the convergence of the Markov chain simulation method
• Markov Random Fields and Their Applications
• Optimum Monte-Carlo sampling using Markov chains
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• A Note on the Re-Use of Samples
• Asymptotic Behavior of the Gibbs Sampler