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hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R

Abstract : An increasing number of time-consuming simulators exhibit a complex noise structure that depends on the inputs. For conducting studies with limited budgets of evaluations, new surrogate methods are required in order to simultaneously model the mean and variance fields. To this end, we present the hetGP package, implementing many recent advances in Gaussian process modeling with input-dependent noise. First, we describe a simple, yet efficient, joint modeling framework that relies on replication for both speed and accuracy. Then we tackle the issue of data acquisition leveraging replication and exploration in a sequential manner for various goals, such as for obtaining a globally accurate model, for optimization, or for contour finding. Reproducible illustrations are provided throughout.
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https://hal.inria.fr/hal-02414688
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Submitted on : Wednesday, December 18, 2019 - 3:34:27 PM
Last modification on : Saturday, June 25, 2022 - 11:41:50 PM
Long-term archiving on: : Thursday, March 19, 2020 - 9:07:39 PM

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Mickael Binois, Robert B. Gramacy. hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R. Journal of Statistical Software, University of California, Los Angeles, 2021, 98 (13), pp.1--44. ⟨10.18637/jss.v098.i13⟩. ⟨hal-02414688⟩

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