Control Variates for Estimation Based on Reversible Markov Chain Monte Carlo Samplers

DOI10.1111/j.1467-9868.2011.01000.xzbMath1411.62056OpenAlexW1548719274MaRDI QIDQ4632668

Ioannis Kontoyiannis, Petros Dellaportas

Publication date: 30 April 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9868.2011.01000.x

zbMATH Keywords

Poisson equationvariance reductionBayesian inferenceMarkov chain Monte Carlo methodslog-linear modelcontrol variatesmixtures of normalshierarchical normal linear modelthreshold auto-regressive model

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Point estimation (62F10) Bayesian inference (62F15) Sampling theory, sample surveys (62D05)

Related Items (12)

An Invitation to Sequential Monte Carlo Samplers ⋮ Variance reduction using nonreversible Langevin samplers ⋮ Stein's method meets computational statistics: a review of some recent developments ⋮ Variance reduction for Markov chains with application to MCMC ⋮ Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization ⋮ Convergence rates for a class of estimators based on Stein's method ⋮ A Perturbative Approach to Control Variates in Molecular Dynamics ⋮ Asymptotic variance for random walk Metropolis chains in high dimensions: logarithmic growth via the Poisson equation ⋮ Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport ⋮ Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC ⋮ Variance reduction for Metropolis-Hastings samplers ⋮ Variance reduction for additive functionals of Markov chains via martingale representations

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