Additive Gaussian Process for Computer Models With Qualitative and Quantitative Factors
Additive Gaussian Process for Computer Models With Qualitative and Quantitative Factors
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DOI：
10.1080/00401706.2016.1211554
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发表时间：
2017-04
期刊：
影响因子：
2.5
通讯作者：
Xinwei Deng;C. D. Lin;K.-W. Liu;R. Rowe
中科院分区：
文献类型：
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作者：
Xinwei Deng;C. D. Lin;K.-W. Liu;R. Rowe
计算机实验在科学和工程的各种应用中经常出现，涉及定性和定量因素。对这些实验的分析还没有完全解决。在这项工作中，我们提出了一个加性高斯过程模型的定性和定量因素的计算机实验。该方法考虑了定性因素的加性相关结构，并假设每个定性因素的相关函数和定量因素的相关函数都是乘性的。它继承了超球分解对定性因素无约束关联结构的灵活性，在计算机实验的复杂系统建模中具有更大的灵活性。数值算例和一个真实的数据应用说明了该方法的优点。本文的补充材料可在网上查阅。
ABSTRACT Computer experiments with qualitative and quantitative factors occur frequently in various applications in science and engineering. Analysis of such experiments is not yet completely resolved. In this work, we propose an additive Gaussian process model for computer experiments with qualitative and quantitative factors. The proposed method considers an additive correlation structure for qualitative factors, and assumes that the correlation function for each qualitative factor and the correlation function of quantitative factors are multiplicative. It inherits the flexibility of unrestrictive correlation structure for qualitative factors by using the hypersphere decomposition, embracing more flexibility in modeling the complex systems of computer experiments. The merits of the proposed method are illustrated by several numerical examples and a real data application. Supplementary materials for this article are available online.