论文标题
EZGP：具有定量和定性因素的计算机实验的易于解释的高斯流程模型
EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors
论文作者
论文摘要
具有定量和定性（QQ）输入的计算机实验通常用于科学和工程应用中。为此类计算机实验构建理想的模拟器仍然是一个具有挑战性的问题。在本文中，我们为计算机实验提出了一个易于解释的高斯流程（EZGP）模型，以反映定性因素不同级别组合下计算机模型的变化。提出的建模策略基于加性高斯工艺，可以灵活地解决涉及多个定性因素的计算机模型的异质性。我们还开发了EZGP模型的两个有用的变体，以实现具有高维度和较大尺寸的数据的计算效率。这些模型的优点通过几个数值示例和一个真实的数据应用来说明。
Computer experiments with both quantitative and qualitative (QQ) inputs are commonly used in science and engineering applications. Constructing desirable emulators for such computer experiments remains a challenging problem. In this article, we propose an easy-to-interpret Gaussian process (EzGP) model for computer experiments to reflect the change of the computer model under the different level combinations of qualitative factors. The proposed modeling strategy, based on an additive Gaussian process, is flexible to address the heterogeneity of computer models involving multiple qualitative factors. We also develop two useful variants of the EzGP model to achieve computational efficiency for data with high dimensionality and large sizes. The merits of these models are illustrated by several numerical examples and a real data application.
