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Visualisation for large-scale Gaussian updates

Journal Article


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Abstract


  • In geostatistics and also in other applications in science and engineering, it is now

    common to perform updates on Gaussian process models with many thousands or even millions

    of components. These large-scale inferences involve modelling, representational and computational

    challenges. We describe a visualization tool for large-scale Gaussian updates, the ‘medal plot’.

    The medal plot shows the updated uncertainty at each observation location and also summarizes

    the sharing of information across observations, as a proxy for the sharing of information across the

    state vector (or latent process). As such, it reflects characteristics of both the observations and the

    statistical model.We illustrate with an application to assess mass trends in the Antarctic Ice Sheet,

    for which there are strong constraints from the observations and the physics.

Publication Date


  • 2016

Citation


  • Rougier, J. & Zammit-Mangion, A. (2016). Visualisation for large-scale Gaussian updates. Scandinavian Journal of Statistics: theory and applications, 43 (4), 1153-1161.

Scopus Eid


  • 2-s2.0-84979769158

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7641&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6611

Number Of Pages


  • 8

Start Page


  • 1153

End Page


  • 1161

Volume


  • 43

Issue


  • 4

Place Of Publication


  • United Kingdom

Abstract


  • In geostatistics and also in other applications in science and engineering, it is now

    common to perform updates on Gaussian process models with many thousands or even millions

    of components. These large-scale inferences involve modelling, representational and computational

    challenges. We describe a visualization tool for large-scale Gaussian updates, the ‘medal plot’.

    The medal plot shows the updated uncertainty at each observation location and also summarizes

    the sharing of information across observations, as a proxy for the sharing of information across the

    state vector (or latent process). As such, it reflects characteristics of both the observations and the

    statistical model.We illustrate with an application to assess mass trends in the Antarctic Ice Sheet,

    for which there are strong constraints from the observations and the physics.

Publication Date


  • 2016

Citation


  • Rougier, J. & Zammit-Mangion, A. (2016). Visualisation for large-scale Gaussian updates. Scandinavian Journal of Statistics: theory and applications, 43 (4), 1153-1161.

Scopus Eid


  • 2-s2.0-84979769158

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7641&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6611

Number Of Pages


  • 8

Start Page


  • 1153

End Page


  • 1161

Volume


  • 43

Issue


  • 4

Place Of Publication


  • United Kingdom