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.
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Kracker, H., Bornkamp, B., Kuhnt, S., Gather, U., Ickstadt, K. (2010). Uncertainty in Gaussian Process Interpolation. In: Devroye, L., Karasözen, B., Kohler, M., Korn, R. (eds) Recent Developments in Applied Probability and Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2598-5_4
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DOI: https://doi.org/10.1007/978-3-7908-2598-5_4
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