Miscellanea. Peskun's theorem and a modified discrete-state Gibbs sampler

DOI10.1093/biomet/83.3.681zbMath0866.62061OpenAlexW2115066119MaRDI QIDQ3837373

Jun S. Liu

Publication date: 8 December 1996

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biomet/83.3.681

zbMATH Keywords

efficiencyMarkov chain Monte CarloIsing modelMetropolis-Hastings algorithmdiscrete-state Gibbs sampling

Mathematics Subject Classification ID

Inference from stochastic processes (62M99)

Related Items (14)

A modified conditional Metropolis-Hastings sampler ⋮ Geometric ergodicity of a more efficient conditional Metropolis-Hastings algorithm ⋮ A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes ⋮ Latent uniform samplers on multivariate binary spaces ⋮ Conditional sequential Monte Carlo in high dimensions ⋮ Bayesian nonparametric density autoregression with lag selection ⋮ A constrained Metropolis-Hastings Robbins-Monro algorithm for \(\boldsymbol{Q}\) matrix estimation in DINA models ⋮ Joint modeling of multiple time series via the beta process with application to motion capture segmentation ⋮ A note on Markov chain Monte Carlo sweep strategies ⋮ An Extension of the Metropolis Algorithm ⋮ Improving efficiency of data augmentation algorithms using Peskun's theorem ⋮ Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning ⋮ Sequential Monte Carlo on large binary sampling spaces ⋮ Peskun-Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario

This page was built for publication: Miscellanea. Peskun's theorem and a modified discrete-state Gibbs sampler