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  • functional_ensemble_sampler-Coullon_Webber_2021

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-021-10004-y

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Ensemble sampler for infinite-dimensional inverse problems

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

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Ensemble sampler for infinite-dimensional inverse problems. / Coullon, J.; Webber, R.J.
In: Statistics and Computing, Vol. 31, No. 3, 28, 15.03.2021.

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

Harvard

Coullon, J & Webber, RJ 2021, 'Ensemble sampler for infinite-dimensional inverse problems', Statistics and Computing, vol. 31, no. 3, 28. https://doi.org/10.1007/s11222-021-10004-y

APA

Coullon, J., & Webber, R. J. (2021). Ensemble sampler for infinite-dimensional inverse problems. Statistics and Computing, 31(3), Article 28. https://doi.org/10.1007/s11222-021-10004-y

Vancouver

Coullon J, Webber RJ. Ensemble sampler for infinite-dimensional inverse problems. Statistics and Computing. 2021 Mar 15;31(3):28. doi: 10.1007/s11222-021-10004-y

Author

Coullon, J. ; Webber, R.J. / Ensemble sampler for infinite-dimensional inverse problems. In: Statistics and Computing. 2021 ; Vol. 31, No. 3.

Bibtex

@article{2770e25207de40c9bba33ddea402ad71,
title = "Ensemble sampler for infinite-dimensional inverse problems",
abstract = "We introduce a new Markov chain Monte Carlo (MCMC) sampler for infinite-dimensional inverse problems. Our new sampler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensemble sampler for the first time to infinite-dimensional function spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our new ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. ",
keywords = "Bayesian inverse problems, Dimensionality reduction, Infinite-dimensional inverse problems, Markov chain Monte Carlo",
author = "J. Coullon and R.J. Webber",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-021-10004-y",
year = "2021",
month = mar,
day = "15",
doi = "10.1007/s11222-021-10004-y",
language = "English",
volume = "31",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - Ensemble sampler for infinite-dimensional inverse problems

AU - Coullon, J.

AU - Webber, R.J.

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-021-10004-y

PY - 2021/3/15

Y1 - 2021/3/15

N2 - We introduce a new Markov chain Monte Carlo (MCMC) sampler for infinite-dimensional inverse problems. Our new sampler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensemble sampler for the first time to infinite-dimensional function spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our new ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable.

AB - We introduce a new Markov chain Monte Carlo (MCMC) sampler for infinite-dimensional inverse problems. Our new sampler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensemble sampler for the first time to infinite-dimensional function spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our new ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable.

KW - Bayesian inverse problems

KW - Dimensionality reduction

KW - Infinite-dimensional inverse problems

KW - Markov chain Monte Carlo

U2 - 10.1007/s11222-021-10004-y

DO - 10.1007/s11222-021-10004-y

M3 - Journal article

VL - 31

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 3

M1 - 28

ER -