Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
dc.contributor.author | Baker, E | |
dc.contributor.author | Challenor, P | |
dc.contributor.author | Eames, M | |
dc.date.accessioned | 2020-06-18T08:56:25Z | |
dc.date.issued | 2020-04-02 | |
dc.description.abstract | The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer models are becoming stochastic, yielding different outputs each time they are run, even if the same input values are used. Stochastic computer models are more difficult to analyse and more difficult to emulate - often requiring substantially more computer model runs to fit. We present a method of using deterministic approximations of the computer model to better construct an emulator. The method is applied to numerous toy examples, as well as an idealistic epidemiology model, and a model from the building performance field. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Published online 7 May 2020 | en_GB |
dc.identifier.doi | 10.1080/10618600.2020.1750416 | |
dc.identifier.uri | http://hdl.handle.net/10871/121509 | |
dc.language.iso | en | en_GB |
dc.publisher | Taylor & Francis | en_GB |
dc.rights | © 2020 The Author(s). Published with license by Taylor and Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_GB |
dc.subject | Emulation | en_GB |
dc.subject | Gaussian process | en_GB |
dc.subject | Heteroscedastic | en_GB |
dc.subject | Multifidelity | en_GB |
dc.subject | Stochastic kriging | en_GB |
dc.subject | Stochastic simulation | en_GB |
dc.title | Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-06-18T08:56:25Z | |
dc.description | This is the final version. Available on open access from Taylor & Francis via the DOI in this record | en_GB |
dc.identifier.eissn | 1537-2715 | |
dc.identifier.journal | Journal of Computational and Graphical Statistics | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-03-02 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-03-02 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-06-18T08:53:45Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-06-18T08:56:34Z | |
refterms.panel | B | en_GB |
refterms.depositException | publishedGoldOA |
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Except where otherwise noted, this item's licence is described as © 2020 The Author(s). Published with
license by Taylor and Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.