Ensemble sampler for infinite-dimensional inverse problems

Coullon, J. and Webber, R.J. (2021) Ensemble sampler for infinite-dimensional inverse problems. Statistics and Computing, 31 (3). ISSN 0960-3174

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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.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-021-10004-y
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
153280
Deposited By:
Deposited On:
29 Mar 2021 11:15
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Jun 2021 04:01