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Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments

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Academic year: 2021

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Figure 2.6: Estimation of β and prediction in the frequentist case. The function x → x 2 on
Figure 2.7: Estimation of β and prediction in the Bayesian case. Same settings as in gure 2.6, where the a priori distribution of β is Gaussian with mean vector (0.2, 0.1) t and diagonal
Figure 2.8: Illustration of the conditional simulations of proposition 2.32. A centered Gaussian process, with Matérn 3
Figure 3.1: Illustration of convergence of the likelihood function in the iid case. Solid lines: plot of realizations of the modied log-likelihood function ψ → L(ψ) in (3.3) for iid Gaussian variables with known variance 1 and unknown mean
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