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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/MagazineJournal articlepeer-review

Published
Article number28
<mark>Journal publication date</mark>15/03/2021
<mark>Journal</mark>Statistics and Computing
Issue number3
Volume31
Number of pages9
Publication StatusPublished
<mark>Original language</mark>English

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.

Bibliographic note

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