Title: Measuring event probabilities in uncertain scalar datasets using Gaussian processes
Authors: Schlegel, Steven
Volke, Sebastian
Scheuermann, Gerik
Citation: WSCG '2016: short communications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 30 - June 3 2016, p. 285-291.
Issue Date: 2016
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2016/!!_CSRN-2602.pdf
ISBN: 978-80-86943-58-9
ISSN: 2464-4617
Keywords: regrese Gaussova procesu;programování OpenCL;údaje o klimatu
Keywords in different language: Gaussian process regression;OpenCL programming;climate data
Abstract: In this paper, we show how the concept of Gaussian process regression can be used to determine potential events in scalar data sets. As a showcase, we will investigate climate data sets in order to identify potential extrem weather events by deriving the probabilities of their appearances. The method is implemented directly on the GPU to ensure interactive frame rates and pixel precise visualizations. We will see, that this approach is especially well suited for sparse sampled data because of its reconstruction properties.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2016: Short Papers Proceedings

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/29715

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