A vanilla Rao-Blackwellization of Metropolis-Hastings algorithms

DOI10.1214/10-AOS838zbMath1209.62023arXiv0904.2144MaRDI QIDQ2429930

Randal Douc, Christian P. Robert Robert

Publication date: 5 April 2011

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

Full work available at URL: https://arxiv.org/abs/0904.2144

zbMATH Keywords

probit modelconditioningcentral limit theoremvariance reductionMarkov chain Monte Carlo (MCMC)

Mathematics Subject Classification ID

Related Items (14)

On parallelizable Markov chain Monte Carlo algorithms with waste-recycling ⋮ Which ergodic averages have finite asymptotic variance? ⋮ Efficient estimation of the link function parameter in a robust Bayesian binary regression model ⋮ Jump Markov chains and rejection-free Metropolis algorithms ⋮ Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC ⋮ On a Metropolis-Hastings importance sampling estimator ⋮ Sampling Constrained Probability Distributions Using Spherical Augmentation ⋮ Optimization via rejection-free partial neighbor search ⋮ Rao–Blackwellisation in the Markov Chain Monte Carlo Era ⋮ Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance ⋮ The importance Markov chain ⋮ On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods ⋮ Bayesian computation: a summary of the current state, and samples backwards and forwards ⋮ Variance reduction of estimators arising from Metropolis-Hastings algorithms

Uses Software

• R
• S-PLUS
• MASS (R)

Cites Work

• Unnamed Item
• Limit theorems for weighted samples with applications to sequential Monte Carlo methods
• Self-regenerative Markov chain Monte Carlo with adaptation
• On convergence of properly weighted samples to the target distribution
• Statistical Analysis of Financial Data in S-Plus
• Does Waste Recycling Really Improve the Multi-Proposal Metropolis–Hastings algorithm? an Analysis Based on Control Variates
• Rao-Blackwellisation of sampling schemes
• Beyond accept-reject sampling

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