Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process

DOI10.1080/03610926.2014.936562zbMath1397.65019arXiv1006.3690OpenAlexW1748283627MaRDI QIDQ2817137

Paul H. Garthwaite, Yanan Fan, Scott A. Sisson

Publication date: 29 August 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1006.3690

zbMATH Keywords

conditional modeMetropolis-Hastings algorithmsrandom-walk\(\alpha\)-mixingtruncated dataBerry-Esseen type bound

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Sums of independent random variables; random walks (60G50) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data, A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density, Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation, Estimation of a functional single index model with dependent errors and unknown error density, Bayesian models for spatial count data with informative finite populations with application to the American community survey, Accelerating adaptation in the adaptive Metropolis–Hastings random walk algorithm, Mixing of MCMC algorithms, A rare event approach to high-dimensional approximate Bayesian computation, Bayesian bandwidth estimation for local linear fitting in nonparametric regression models, Simultaneous fitting of Bayesian penalised quantile splines, A Bayesian procedure for bandwidth selection in circular kernel density estimation, Estimation and uncertainty quantification for extreme quantile regions, Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density, Scalable Bayesian inference for the inverse temperature of a hidden Potts model, Bayesian Inference for the One-Factor Copula Model, Updating variational Bayes: fast sequential posterior inference, Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins, Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density

Cites Work

• On the stability and ergodicity of adaptive scaling Metropolis algorithms
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Optimal scaling for various Metropolis-Hastings algorithms.
• Generalised linear mixed model analysis via sequential Monte Carlo sampling
• General design Bayesian generalized linear mixed models
• Componentwise adaptation for high dimensional MCMC
• On a stochastic approximation procedure based on averaging
• Semiparametric regression for clustered data
• Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC
• Handbook of Markov Chain Monte Carlo
• Confidence Intervals from Randomization Tests
• Prepivoting by weighted bootstrap iteration
• Bias correction in generalised linear mixed models with a single component of dispersion
• Generating Monte Carlo Confidence Intervals by the Robbins-Monro Process
• A Stochastic Approximation Method
• An adaptive Metropolis algorithm
• Unnamed Item