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Uncertainty in Gaussian Process Interpolation

In: Recent Developments in Applied Probability and Statistics

Author

Listed:
  • Hilke Kracker

    (TU Dortmund, Fakultät Statistik)

  • Björn Bornkamp

    (TU Dortmund, Fakultät Statistik)

  • Sonja Kuhnt

    (TU Dortmund, Fakultät Statistik)

  • Ursula Gather

    (TU Dortmund, Fakultät Statistik)

  • Katja Ickstadt

    (TU Dortmund, Fakultät Statistik)

Abstract

In this article, we review a probabilistic method for multivariate interpolation based on Gaussian processes. This method is currently a standard approach for approximating complex computer models in statistics, and one of its advantages is the fact that it accompanies the predicted values with uncertainty statements. We focus on investigating the reliability of the method’s uncertainty statements in a simulation study. For this purpose we evaluate the effect of different objective priors and different computational approaches. We illustrate the interpolation method and the practical importance of uncertainty quantification in interpolation in a sequential design application in sheet metal forming. Here design points are added sequentially based on uncertainty statements.

Suggested Citation

  • Hilke Kracker & Björn Bornkamp & Sonja Kuhnt & Ursula Gather & Katja Ickstadt, 2010. "Uncertainty in Gaussian Process Interpolation," Springer Books, in: Luc Devroye & Bülent Karasözen & Michael Kohler & Ralf Korn (ed.), Recent Developments in Applied Probability and Statistics, pages 79-102, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2598-5_4
    DOI: 10.1007/978-3-7908-2598-5_4
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