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Depositordc.contributorGair, Jonathan R
Funderdc.contributor.otherSTFC - Science and Technology Facilities Councilen_UK
Funderdc.contributor.otherUniversity of Edinburghen_UK
Data Creatordc.creatorMoore, Christopher J
Data Creatordc.creatorChua, Alvin J K
Data Creatordc.creatorBerry, Christopher P L
Data Creatordc.creatorGair, Jonathan R
Date Accessioneddc.date.accessioned2016-02-12T12:05:57Z
Date Availabledc.date.available2016-02-12T12:05:57Z
Citationdc.identifier.citationMoore, Christopher J; Chua, Alvin J K; Berry, Christopher P L; Gair, Jonathan R. (2016). Fast methods for training Gaussian processes, [software]. https://doi.org/10.7488/ds/1343.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/1924
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/1343
Dataset Description (abstract)dc.description.abstractThis submission includes a simplified version of some code we have been developing for fast training of Gaussian processes. We also include a sample data set, which is NOAA tidal data from Woods Hole in the US, downloaded from http://tidesandcurrents.noaa.gov/ . The code and data included here were used to produce the numerical results in the following paper: [1] Fast methods for training Gaussian processes, C. J. Moore, A. J. K. Chua, C. P. L. Berry, and J. R. Gair (2016), submitted to RSOS.en_UK
Languagedc.language.isoengen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyFast methods for training Gaussian processes, C. J. Moore, A. J. K. Chua, C. P. L. Berry, and J. R. Gair (2016), submitted to RSOS.en_UK
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Sourcedc.sourceNOAA tidal data: http://tidesandcurrents.noaa.gov/en_UK
Subjectdc.subjectGaussian processesen_UK
Subject Classificationdc.subject.classificationMathematical and Computer Sciences::Applied Statisticsen_UK
Titledc.titleFast methods for training Gaussian processesen_UK
Typedc.typesoftwareen_UK

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