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Best Linear Unbiased Prediction for Multifidelity Computer Experiments

机译:多保真度计算机实验的最佳线性无偏预测

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摘要

Recently it becomes a growing trend to study complex systems which contain multiple computer codes with different levels of accuracy, and a number of hierarchical Gaussian process models are proposed to handle such multiple-fidelity codes. This paper derives the best linear unbiased prediction for three popular classes of multiple-level Gaussian process models. The predictors all have explicit expressions at each untried point. Empirical best linear unbiased predictors are also provided by plug-in methods with generalized maximum likelihood estimators of unknown parameters.
机译:最近,研究包含多个具有不同精度级别的计算机代码的复杂系统成为一种日益增长的趋势,并且提出了许多分层的高斯过程模型来处理这种多保真度代码。本文针对三种流行的多级高斯过程模型,得出了最佳的线性无偏预测。预测变量在每个未尝试的点都有明确的表达式。插入式方法还提供了经验最佳的线性无偏预测器,其中包含未知参数的广义最大似然估计器。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|8525736.1-8525736.7|共7页
  • 作者单位

    Beijing Univ Civil Engn & Architecture, Sch Sci, Beijing 100044, Peoples R China;

    Beijing Univ Civil Engn & Architecture, Sch Sci, Beijing 100044, Peoples R China;

    Beijing Univ Civil Engn & Architecture, Sch Sci, Beijing 100044, Peoples R China;

    Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, Beijing 100190, Peoples R China;

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