Comparing sweep strategies for stochastic relaxation

DOI10.1016/0047-259X(91)90080-LzbMath0735.60035MaRDI QIDQ1176229

Ulf Grenander, Yali Amit

Publication date: 25 June 1992

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

rate of convergencerandom sweep strategiesstochastic relaxation procedure

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Limit theorems in probability theory (60F99)

Related Items (20)

Applications of geometric bounds to the convergence rate of Markov chains on \(\mathbb R^ {n}\). ⋮ Convergence rates of two-component MCMC samplers ⋮ Outperforming the Gibbs sampler empirical estimator for nearest-neighbor random fields ⋮ Optimizing random scan Gibbs samplers ⋮ Markov chain convergence: From finite to infinite ⋮ Convergence properties of the Gibbs sampler for perturbations of Gaussians ⋮ On the performance of the gibbs sampler for the multivariate normal distribution ⋮ Approximately counting and sampling knowledge states ⋮ On rates of convergence of stochastic relaxation for Gaussian and non- Gaussian distributions ⋮ Asymptotic behavior of eigenvalues and random updating schemes ⋮ A note on Markov chain Monte Carlo sweep strategies ⋮ Implementing random scan Gibbs samplers ⋮ Accelerating diffusions ⋮ Attaining the optimal Gaussian diffusion acceleration ⋮ Simulating large Gaussian random vectors subject to inequality constraints by Gibbs sampling ⋮ On the geometrical convergence of Gibbs sampler in \(\mathbb R^d\) ⋮ Coordinate selection rules for Gibbs sampling ⋮ Polynomial Accelerated Solutions to a Large Gaussian Model for Imaging Biofilms: In Theory and Finite Precision ⋮ Use of the Gibbs sampler to invert large, possibly sparse, positive definite matrices ⋮ Jump-diffusion Markov processes on orthogonal groups for object pose estimation

Cites Work

• Pattern synthesis. Lectures in pattern theory. Vol. I
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Equation of State Calculations by Fast Computing Machines
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