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dc.contributor.authorPaglia, Jacopo
dc.contributor.authorEidsvik, Jo
dc.contributor.authorKarvanen, Juha
dc.date.accessioned2021-11-08T07:44:03Z
dc.date.available2021-11-08T07:44:03Z
dc.date.issued2022
dc.identifier.citationPaglia, J., Eidsvik, J., & Karvanen, J. (2022). Efficient spatial designs using Hausdorff distances and Bayesian optimization. <i>Scandinavian Journal of Statistics</i>, <i>49</i>(3), 1060-1084. <a href="https://doi.org/10.1111/sjos.12554" target="_blank">https://doi.org/10.1111/sjos.12554</a>
dc.identifier.otherCONVID_101194313
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78515
dc.description.abstractAn iterative Bayesian optimisation technique is presented to find spatial designs of data that carry much information. We use the decision theoretic notion of value of information as the design criterion. Gaussian process surrogate models enable fast calculations of expected improvement for a large number of designs, while the full-scale value of information evaluations are only done for the most promising designs. The Hausdorff distance is used to model the similarity between designs in the surrogate Gaussian process covariance representation, and this allows the suggested algorithm to learn across different designs. We study properties of the Bayesian optimisation design algorithm in a synthetic example and real-world examples from forest conservation and petroleum drilling operations. In the synthetic example we consider a model where the exact solution is available and we run the algorithm under different versions of this example and compare it with existing approaches such as sequential selection and an exchange algorithm.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofseriesScandinavian Journal of Statistics
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherBayesian optimisation
dc.subject.otherHausdorff distance
dc.subject.othervalue of information
dc.titleEfficient spatial designs using Hausdorff distances and Bayesian optimization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202111085538
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1060-1084
dc.relation.issn0303-6898
dc.relation.numberinseries3
dc.relation.volume49
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysobayesilainen menetelmä
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysooptimointi
dc.subject.ysopaikkatietoanalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p28516
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1111/sjos.12554
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationJacopo Paglia’s and Jo Eidsvik’s work are supported by the KPN project 255418/E30: "Reduced uncertainty in overpressures and drilling window prediction ahead of the bit (PressureAhead)", of the Norwegian Research Council and the DrillWell Centre (AkerBP, Wintershall, ConocoPhillips and Equinor). Juha Karvanen’s work is supported by Grant number 311877 "Decision analytics utilizing causal models and multiobjective optimisation" (DEMO), of the Academy of Finland


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