Constructing a simulation surrogate with partially observed output

IF 2.3 3区 工程技术 Q1 STATISTICS & PROBABILITY Technometrics Pub Date : 2023-04-19 DOI:10.1080/00401706.2023.2210170
Moses Y H Chan, M. Plumlee, Stefan M. Wild
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引用次数: 0

Abstract

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.
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构造具有部分观测输出的模拟代理
高斯过程替代是直接使用计算成本高的模拟模型的流行替代方法。当模拟输出包含许多响应时,通常采用降维技术来构建这些代理。然而,降维的替代方法通常依赖于完整的训练数据输出。本文提出了一种新的高斯过程替代方法,允许使用部分观察到的输出,同时保持计算效率。该方法包括缺失值的输入和用于高斯过程推理的协方差矩阵的调整。结果代理表示可用的响应,忽略缺失的响应,并提供有意义的不确定性量化。在模拟研究和案例研究中,所提出的方法被证明比其他方法提供更清晰的推断,其中能量密度函数模型经常返回不完整输出进行校准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Technometrics
Technometrics 管理科学-统计学与概率论
CiteScore
4.50
自引率
16.00%
发文量
59
审稿时长
>12 weeks
期刊介绍: Technometrics is a Journal of Statistics for the Physical, Chemical, and Engineering Sciences, and is published Quarterly by the  American Society for Quality and the American Statistical Association.Since its inception in 1959, the mission of Technometrics has been to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences.
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