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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version L F South, T Karvonen, C Nemeth, M Girolami, C J Oates, Semi-exact control functionals from Sard’s method, Biometrika, Volume 109, Issue 2, June 2022, Pages 351–367 is available online at: https://academic.oup.com/biomet/article/109/2/351/6309456

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Semi-Exact Control Functionals From Sard's Method

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Semi-Exact Control Functionals From Sard's Method. / South, Leah; Karvonen, Toni; Nemeth, Christopher et al.
In: Biometrika, Vol. 109, No. 2, 30.06.2022, p. 351-367.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

South, L, Karvonen, T, Nemeth, C, Girolami, M & Oates, CJ 2022, 'Semi-Exact Control Functionals From Sard's Method', Biometrika, vol. 109, no. 2, pp. 351-367. https://doi.org/10.1093/biomet/asab036

APA

South, L., Karvonen, T., Nemeth, C., Girolami, M., & Oates, C. J. (2022). Semi-Exact Control Functionals From Sard's Method. Biometrika, 109(2), 351-367. https://doi.org/10.1093/biomet/asab036

Vancouver

South L, Karvonen T, Nemeth C, Girolami M, Oates CJ. Semi-Exact Control Functionals From Sard's Method. Biometrika. 2022 Jun 30;109(2):351-367. Epub 2021 Jun 25. doi: 10.1093/biomet/asab036

Author

South, Leah ; Karvonen, Toni ; Nemeth, Christopher et al. / Semi-Exact Control Functionals From Sard's Method. In: Biometrika. 2022 ; Vol. 109, No. 2. pp. 351-367.

Bibtex

@article{4fc438564c9b4a5ca7fc9f9a1101a87d,
title = "Semi-Exact Control Functionals From Sard's Method",
abstract = "A novel control variate technique is proposed for post-processing of Markov chain Monte Carlo output, based both on Stein's method and an approach to numerical integration due to Sard. The resulting estimators of posterior expected quantities of interest are proven to be polynomially exact in the Gaussian context, while empirical results suggest the estimators approximate a Gaussian cubature method near the Bernstein-von-Mises limit. The main theoretical result establishes a bias-correction property in settings where the Markov chain does not leave the posterior invariant.Empirical results are presented across a selection of Bayesian inference tasks. All methods used in this paper are available in the R package ZVCV.",
author = "Leah South and Toni Karvonen and Christopher Nemeth and Mark Girolami and Oates, {Chris J.}",
note = "This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version L F South, T Karvonen, C Nemeth, M Girolami, C J Oates, Semi-exact control functionals from Sard{\textquoteright}s method, Biometrika, Volume 109, Issue 2, June 2022, Pages 351–367 is available online at: https://academic.oup.com/biomet/article/109/2/351/6309456 ",
year = "2022",
month = jun,
day = "30",
doi = "10.1093/biomet/asab036",
language = "English",
volume = "109",
pages = "351--367",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",

}

RIS

TY - JOUR

T1 - Semi-Exact Control Functionals From Sard's Method

AU - South, Leah

AU - Karvonen, Toni

AU - Nemeth, Christopher

AU - Girolami, Mark

AU - Oates, Chris J.

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version L F South, T Karvonen, C Nemeth, M Girolami, C J Oates, Semi-exact control functionals from Sard’s method, Biometrika, Volume 109, Issue 2, June 2022, Pages 351–367 is available online at: https://academic.oup.com/biomet/article/109/2/351/6309456

PY - 2022/6/30

Y1 - 2022/6/30

N2 - A novel control variate technique is proposed for post-processing of Markov chain Monte Carlo output, based both on Stein's method and an approach to numerical integration due to Sard. The resulting estimators of posterior expected quantities of interest are proven to be polynomially exact in the Gaussian context, while empirical results suggest the estimators approximate a Gaussian cubature method near the Bernstein-von-Mises limit. The main theoretical result establishes a bias-correction property in settings where the Markov chain does not leave the posterior invariant.Empirical results are presented across a selection of Bayesian inference tasks. All methods used in this paper are available in the R package ZVCV.

AB - A novel control variate technique is proposed for post-processing of Markov chain Monte Carlo output, based both on Stein's method and an approach to numerical integration due to Sard. The resulting estimators of posterior expected quantities of interest are proven to be polynomially exact in the Gaussian context, while empirical results suggest the estimators approximate a Gaussian cubature method near the Bernstein-von-Mises limit. The main theoretical result establishes a bias-correction property in settings where the Markov chain does not leave the posterior invariant.Empirical results are presented across a selection of Bayesian inference tasks. All methods used in this paper are available in the R package ZVCV.

U2 - 10.1093/biomet/asab036

DO - 10.1093/biomet/asab036

M3 - Journal article

VL - 109

SP - 351

EP - 367

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 2

ER -