dc.contributor.author |
Cui, T |
en |
dc.contributor.author |
Fox, C |
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dc.contributor.author |
O'Sullivan, Michael |
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dc.date.accessioned |
2019-06-19T01:52:51Z |
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dc.date.issued |
2019-06-08 |
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dc.identifier.citation |
International Journal for Numerical Methods in Engineering 118(10):578-605 08 Jun 2019 |
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dc.identifier.issn |
0029-5981 |
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dc.identifier.uri |
http://hdl.handle.net/2292/47186 |
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dc.description.abstract |
Sample‐based Bayesian inference provides a route to uncertainty quantification in the geosciences and inverse problems in general but is very computationally demanding in the naïve form, which requires simulating an accurate computer model at each iteration. We present a new approach that constructs a stochastic correction to the error induced by a reduced model, with the correction improving as the algorithm proceeds. This enables sampling from the correct target distribution at reduced computational cost per iteration, as in existing delayed‐acceptance schemes, while avoiding appreciable loss of statistical efficiency that necessarily occurs when using a reduced model. Use of the stochastic correction significantly reduces the computational cost of estimating quantities of interest within desired uncertainty bounds. In contrast, existing schemes that use a reduced model directly as a surrogate do not actually improve computational efficiency in our target applications. We build on recent simplified conditions for adaptive Markov chain Monte Carlo algorithms to give practical approximation schemes and algorithms with guaranteed convergence. The efficacy of this new approach is demonstrated in two computational examples, including calibration of a large‐scale numerical model of a real geothermal reservoir, that show good computational and statistical efficiencies on both synthetic and measured data sets. |
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dc.publisher |
Wiley |
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dc.relation.ispartofseries |
International Journal for Numerical Methods in Engineering |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
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dc.rights |
This is the peer reviewed version of the following article:International Journal for Numerical Methods in Engineering 118(10):578-605 08 Jun 2019, which has been published in final form at http://dx.doi.org/10.1002/nme.6028. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. |
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dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.rights.uri |
https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html |
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dc.title |
A posteriori stochastic correction of reduced models in delayed acceptance MCMC, with application to multiphase subsurface inverse problems |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.1002/nme.6028 |
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pubs.issue |
10 |
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pubs.begin-page |
578 |
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pubs.volume |
118 |
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dc.rights.holder |
Copyright: John Wiley & Sons, Ltd. |
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pubs.end-page |
605 |
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pubs.publication-status |
Published |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
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pubs.subtype |
Article |
en |
pubs.elements-id |
764319 |
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pubs.org-id |
Engineering |
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pubs.org-id |
Engineering Science |
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pubs.record-created-at-source-date |
2019-03-01 |
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