Variance reduction for Markov chains with application to MCMC

DOI10.1007/s11222-020-09931-zzbMath1447.62107arXiv1910.03643OpenAlexW3007659715MaRDI QIDQ2195839

Denis Belomestny, Eric Moulines, Leonid Iosipoi, S. P. Samsonov, Alexey Naumov

Publication date: 27 August 2020

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.03643

zbMATH Keywords

Markov chain Monte Carlovariance reductionMetropolis-adjusted Langevin algorithmrandom walk Metropolisunadjusted Langevin algorithmempirical spectral variance minimizationStein's control variates

Mathematics Subject Classification ID

Inference from stochastic processes and spectral analysis (62M15) Monte Carlo methods (65C05) Anomalous diffusion models (subdiffusion, superdiffusion, continuous-time random walks, etc.) (60K50)

Related Items (10)

Empirical variance minimization with applications in variance reduction and optimal control ⋮ A Riemann-Stein kernel method ⋮ Stein's method meets computational statistics: a review of some recent developments ⋮ Markov chain stochastic DCA and applications in deep learning with PDEs regularization ⋮ Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization ⋮ Optimal friction matrix for underdamped Langevin sampling ⋮ Control variate selection for Monte Carlo integration ⋮ 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

Uses Software

• BayesDA

Cites Work

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