Weak convergence of Metropolis algorithms for non-I.I.D. target distributions
type: Item
instance of: scholarly article
title: Weak convergence of Metropolis algorithms for non-I.I.D. target distributions
zbMATH Open document ID: 1144.60016
DOI: 10.1214/105051607000000096
author: M. Bédard
published in: The Annals of Applied Probability
publication date: 2008-01-28T00:00:00Z
full work available at URL: https://arxiv.org/abs/0710.3684
Mathematics Subject Classification ID: 60F05
Mathematics Subject Classification ID: 65C40
zbMATH DE Number: 5230205
zbMATH Keywords: Metropolis algorithm
zbMATH Keywords: weak convergence
zbMATH Keywords: optimal scaling
zbMATH Keywords: diffusion
zbMATH Keywords: Markov chain Monte Carlo
MaRDI profile type: Publication
OpenAlex ID: W3100216688
arXiv ID: 0710.3684
cites work: Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234
cites work: Q4272783
cites work: Bayesian computation and stochastic systems. With comments and reply.
cites work: Optimal scaling of MaLa for nonlinear regression.
cites work: From Metropolis to diffusions: Gibbs states and optimal scaling.
cites work: Q3721531
cites work: Monte Carlo sampling methods using Markov chains and their applications
cites work: Optimal scaling for partially updating MCMC algorithms
cites work: Weak convergence and optimal scaling of random walk Metropolis algorithms
cites work: Optimal Scaling of Discrete Approximations to Langevin Diffusions
cites work: Optimal scaling for various Metropolis-Hastings algorithms.
cites work: General state space Markov chains and MCMC algorithms
cites work: Q4492951
description: scientific article