A Clustered Gaussian Process Model for Computer Experiments
A Clustered Gaussian Process Model for Computer Experiments
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DOI：
10.5705/ss.202020.0456
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发表时间：
2020-03
影响因子：
1.4
通讯作者：
Chih-Li Sung;Benjamin Haaland;Youngdeok Hwang;Siyuan Lu
中科院分区：
文献类型：
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作者：
Chih-Li Sung;Benjamin Haaland;Youngdeok Hwang;Siyuan Lu
高斯过程一直是计算机模拟的重要方法之一。然而，高斯过程的平稳性假设和大规模数据集的复杂性限制了其在实践中的可用性。在本文中，我们提出了一个聚类高斯过程模型，该模型将输入数据分割成多个簇，在每个簇中执行一个高斯过程。采用随机期望最大化方法对模型进行有效拟合。在我们的模拟以及太阳辐照度模拟的实际应用中，我们提出的方法比主要竞争对手的均方误差更小，计算时间更短，并且通过发现簇提供了有价值的数据见解。
A Gaussian process has been one of the important approaches for emulating computer simulations. However, the stationarity assumption for a Gaussian process and the intractability for large-scale dataset limit its availability in practice. In this article, we propose a clustered Gaussian process model which segments the input data into multiple clusters, in each of which a Gaussian process is performed. The stochastic expectation-maximization is employed to efficiently fit the model. In our simulations as well as a real application to solar irradiance emulation, our proposed method had smaller mean square error than its main competitors, with competitive computation time, and provides valuable insights from data by discovering the clusters.