A generalization of inverse distance weighting and an equivalence relationship to noise-free Gaussian process interpolation via Riesz representation theorem

DOI10.1080/03081087.2017.1337057zbMath1391.65032OpenAlexW2622957456MaRDI QIDQ4643710

Geert Verbeke, Geert Molenberghs, Wim De Mulder

Publication date: 28 May 2018

Published in: Linear and Multilinear Algebra (Search for Journal in Brave)

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/594846

zbMATH Keywords

interpolationRiesz representation theoremGaussian processinverse distance weighting

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Numerical interpolation (65D05) Quadratic and bilinear forms, inner products (15A63)

Related Items (1)

Max-product Shepard operators based on multivariable Taylor polynomials

Cites Work

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• Interpolation of spatial data. Some theory for kriging
• Approximating potential integrals by cardinal basis interpolants on multivariate scattered data
• Bayesian learning for neural networks
• The effect of the nugget on Gaussian process emulators of computer models
• Surrogate-Based Modeling and Optimization

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