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dc.contributor.authorChristianson, B.
dc.contributor.authorCox, M.
dc.date.accessioned2009-06-17T10:48:05Z
dc.date.available2009-06-17T10:48:05Z
dc.date.issued2006
dc.identifier.citationChristianson , B & Cox , M 2006 , ' Automatic Propagation of Uncertainties ' , Lecture Notes in Computational Science and Engineering , vol. 50 , pp. 47-58 . https://doi.org/10.1007/3-540-28438-9_4
dc.identifier.issn1439-7358
dc.identifier.otherPURE: 96317
dc.identifier.otherPURE UUID: a901ff24-347f-4d4b-a494-47daf4af99ed
dc.identifier.otherdspace: 2299/3600
dc.identifier.otherScopus: 84880370156
dc.identifier.urihttp://hdl.handle.net/2299/3600
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer.
dc.description.abstractMotivated by problems in metrology, we consider a numerical evaluation program y = f(x) as a model for a measurement process. We use a probability density function to represent the uncertainties in the inputs x and examine some of the consequences of using Automatic Differentiation to propagate these uncertainties to the outputs y.We show how to use a combination of Taylor series propagation and interval partitioning to obtain coverage (confidence) intervals and ellipsoids based on unbiased estimators for means and covariances of the outputs, even where f is sharply non-linear, and even when the level of probability required makes the use of Monte Carlo techniques computationally problematic.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computational Science and Engineering
dc.titleAutomatic Propagation of Uncertaintiesen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionofrecordhttps://doi.org/10.1007/3-540-28438-9_4
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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