Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases
利用校准最优基量化空间输出计算机模型的不确定性
The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the “terminal case,” in which the model cannot reproduce observations…