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Control functionals for Monte Carlo integration

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  • Chris J. Oates
  • Mark Girolami
  • Nicolas Chopin

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  • Chris J. Oates & Mark Girolami & Nicolas Chopin, 2017. "Control functionals for Monte Carlo integration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 695-718, June.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:3:p:695-718
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    File URL: http://hdl.handle.net/10.1111/rssb.12185
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    1. Jean-Marie Cornuet & Jean-Michel Marin & Antonietta Mira & Christian P. Robert, 2012. "Adaptive Multiple Importance Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(4), pages 798-812, December.
    2. Reuven Y. Rubinstein & Ruth Marcus, 1985. "Efficiency of Multivariate Control Variates in Monte Carlo Simulation," Operations Research, INFORMS, vol. 33(3), pages 661-677, June.
    3. Michael B. Giles & Lukasz Szpruch, 2012. "Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without L\'{e}vy area simulation," Papers 1202.6283, arXiv.org, revised May 2014.
    4. Ghosh, Joyee & Clyde, Merlise A., 2011. "Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1041-1052.
    5. repec:dau:papers:123456789/3578 is not listed on IDEAS
    6. Petros Dellaportas & Ioannis Kontoyiannis, 2012. "Control variates for estimation based on reversible Markov chain Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 133-161, January.
    7. repec:dau:papers:123456789/10690 is not listed on IDEAS
    8. Hugo Hammer & Håkon Tjelmeland, 2008. "Control Variates for the Metropolis–Hastings Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 400-414, September.
    9. Chris J. Oates & Theodore Papamarkou & Mark Girolami, 2016. "The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 634-645, April.
    10. N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607, July.
    11. Wentao Li & Rong Chen & Zhiqiang Tan, 2016. "Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 298-313, March.
    12. Wentao Li & Zhiqiang Tan & Rong Chen, 2013. "Two-Stage Importance Sampling With Mixture Proposals," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1350-1365, December.
    13. Mark Girolami & Ben Calderhead, 2011. "Riemann manifold Langevin and Hamiltonian Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 123-214, March.
    14. Calderhead, Ben & Girolami, Mark, 2009. "Estimating Bayes factors via thermodynamic integration and population MCMC," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4028-4045, October.
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    Cited by:

    1. Leluc, Rémi & Portier, François & Segers, Johan & Zhuman, Aigerim, 2022. "A Quadrature Rule combining Control Variates and Adaptive Importance Sampling," LIDAM Discussion Papers ISBA 2022018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Belomestny, Denis & Goldman, Artur & Naumov, Alexey & Samsonov, Sergey, 2024. "Theoretical guarantees for neural control variates in MCMC," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 382-405.
    3. L F South & T Karvonen & C Nemeth & M Girolami & C J Oates, 2022. "Semi-exact control functionals from Sard’s method [Zero-variance principle for Monte Carlo algorithms]," Biometrika, Biometrika Trust, vol. 109(2), pages 351-367.
    4. Jean-Jacques Forneron, 2019. "A Scrambled Method of Moments," Papers 1911.09128, arXiv.org.
    5. Leluc, Rémi & Dieuleveut, Aymeric & Portier, François & Segers, Johan & Zhuman, Aigerim, 2024. "Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates," LIDAM Discussion Papers ISBA 2024003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Pierre E. Jacob & John O’Leary & Yves F. Atchadé, 2020. "Unbiased Markov chain Monte Carlo methods with couplings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 543-600, July.
    7. Linda Chamakh & Zoltan Szabo, 2021. "Keep it Tighter -- A Story on Analytical Mean Embeddings," Papers 2110.09516, arXiv.org, revised Nov 2024.
    8. Marina Riabiz & Wilson Ye Chen & Jon Cockayne & Pawel Swietach & Steven A. Niederer & Lester Mackey & Chris. J. Oates, 2022. "Optimal thinning of MCMC output," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1059-1081, September.
    9. Chamakh, Linda & Szabo, Zoltan, 2021. "Kernel minimum divergence portfolios," LSE Research Online Documents on Economics 115723, London School of Economics and Political Science, LSE Library.
    10. Marc Sabate Vidales & David Siska & Lukasz Szpruch, 2018. "Unbiased deep solvers for linear parametric PDEs," Papers 1810.05094, arXiv.org, revised Jan 2022.
    11. Richard A. Davis & Thiago do Rêgo Sousa & Claudia Klüppelberg, 2021. "Indirect inference for time series using the empirical characteristic function and control variates," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 653-684, September.

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