Control Variates for the Metropolis–Hastings Algorithm
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Publication:5324876
DOI10.1111/j.1467-9469.2008.00601.xzbMath1199.65018OpenAlexW1985217674MaRDI QIDQ5324876
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
Markov chain Monte Carlovariance reductionMetropolis-Hastings algorithmmathematical expectationbiological datarejected stateTokyo rainfalls data
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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
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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