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Scaled Vecchia Approximation for Fast Computer-Model Emulation

机译：用于快速计算机模型仿真的缩放 Vecchia 近似

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摘要

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia's ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.
机译：许多科学现象进行了研究计算机实验组成的多个运行一个计算机模型,不同的输入设置。工具,计算机实验的分析,使输入设置之间的插值,但是直接GP推理计算大型数据集的不可行。扩展一个强大的类GP的方法使可伸缩的空间统计数据大型计算机的分析和仿真实验。要求条件近似的将输入空间,每个输入了根据强烈相关(制作)的反应。从数据估计参数的全科医生协方差函数使用费舍尔得分。方法是高度可伸缩的,使估计,在近似线性联合预测和仿真运行时间的数量模型。数值例子,我们的方法优于现有方法。

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