Classical quadrature rules via Gaussian processes

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Karvonen, Toni
dc.contributor.author Särkkä, Simo
dc.date.accessioned 2018-03-16T10:31:33Z
dc.date.available 2018-03-16T10:31:33Z
dc.date.issued 2017-12-07
dc.identifier.citation Karvonen , T & Särkkä , S 2017 , Classical quadrature rules via Gaussian processes . in Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017 . IEEE International Workshop on Machine Learning for Signal Processing , Institute of Electrical and Electronics Engineers Inc. , IEEE International Workshop on Machine Learning for Signal Processing , Tokyo , Japan , 25/09/2017 . DOI: 10.1109/MLSP.2017.8168195 en
dc.identifier.isbn 978-1-5090-6341-3
dc.identifier.issn 2161-0363
dc.identifier.issn 2161-0371
dc.identifier.other PURE UUID: 34197e56-4c1e-4bbd-9e78-d2f1e44a0389
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/classical-quadrature-rules-via-gaussian-processes(34197e56-4c1e-4bbd-9e78-d2f1e44a0389).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/15931102/KarvonenSarkka2017_MLSP.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/30253
dc.description.abstract In an extension to some previous work on the topic, we show how all classical polynomial-based quadrature rules can be interpreted as Bayesian quadrature rules if the covariance kernel is selected suitably. As the resulting Bayesian quadrature rules have zero posterior integral variance, the results of this article are mostly of theoretical interest in clarifying the relationship between the two different approaches to numerical integration. en
dc.format.extent 7
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE
dc.relation.ispartof IEEE International Workshop on Machine Learning for Signal Processing en
dc.relation.ispartofseries Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017 en
dc.relation.ispartofseries IEEE International Workshop on Machine Learning for Signal Processing en
dc.rights openAccess en
dc.subject.other 111 Mathematics en
dc.title Classical quadrature rules via Gaussian processes en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.subject.keyword 111 Mathematics
dc.identifier.urn URN:NBN:fi:aalto-201803161723
dc.identifier.doi 10.1109/MLSP.2017.8168195
dc.type.version acceptedVersion


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