Control Variates for the Metropolis–Hastings Algorithm

DOI10.1111/j.1467-9469.2008.00601.xzbMath1199.65018OpenAlexW1985217674MaRDI QIDQ5324876

Hugo Hammer, Håkon Tjelmeland

Publication date: 8 August 2009

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: http://cds.cern.ch/record/975803

zbMATH Keywords

Markov chain Monte Carlovariance reductionMetropolis-Hastings algorithmmathematical expectationbiological datarejected stateTokyo rainfalls data

Mathematics Subject Classification ID

Related Items (5)

Zero variance differential geometric Markov chain Monte Carlo algorithms ⋮ Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization ⋮ Convergence rates for a class of estimators based on Stein's method ⋮ Exploiting multi-core architectures for reduced-variance estimation with intractable likelihoods ⋮ Variance reduction for Metropolis-Hastings samplers

Uses Software

• GMRFLib

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Mode Jumping Proposals in MCMC
• Optimum Monte-Carlo sampling using Markov chains
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Rao-Blackwellisation of sampling schemes
• Finite Mixture Models for Proportions
• A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL-mapping
• Monte Carlo sampling methods using Markov chains and their applications
• Monte Carlo strategies in scientific computing
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