Some recent insights into computing with positive definite kernels | Vidéo | Carmin.tv

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Some recent insights into computing with positive definite kernels

By Greg Fasshauer

Appears in collections : Multivariate approximation and interpolation with applications - MAIA / Approximation et interpolation à plusieurs variables et applications - MAIA, Exposés de recherche

In this talk I will discuss recent joint work with Mike McCourt (SigOpt, San Francisco) that has led to progress on the numerically stable computation of certain quantities of interest when working with positive definite kernels to solve scattered data interpolation (or kriging) problems. In particular, I will draw upon insights from both numerical analysis and modeling with Gaussian processes which will allow us to connect quantities such as, e.g., (deterministic) error estimates in terms of the power function with the kriging variance. This provides new kernel parametrization criteria as well as new ways to compute known criteria such as MLE. Some numerical examples will illustrate the effectiveness of this approach.

Information about the video

Date of recording 21/09/2016
Date of publication 27/09/2016
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19052703
Cite this video Fasshauer, Greg (21/09/2016). Some recent insights into computing with positive definite kernels. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19052703
URL https://dx.doi.org/10.24350/CIRM.V.19052703

Domain(s)

Bibliography

Fasshauer, G., & McCourt, M. (2015). Kernel-based approximation methods using MATLAB. Hackensack, NJ: World Scientific - http://www.worldscientific.com/worldscibooks/10.1142/9335
McCourt, M., & Fasshauer, G. (submitted for publication). Stable likelihood computation for Gaussian random fields -

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