Abstract Statistically modeling the output of a stochastic computer model can be difficult to do accurately without a large simulation budget. We alleviate this problem by exploiting readily available deterministic approximations to efficiently learn about the respective stochastic computer models. This is done via the summation of two Gaussian processes; one responsible for modeling the deterministic approximation, the other responsible for using such approximation to better statistically model the stochastic computer model. The developed method provides high predictive performance and increased confidence that complicated features of a stochastic computer model are captured, even when the simulation budget is small. Several synthetic computer models are used to outline the capabilities of this method, and two real-world examples are used to display its practical utility. Supplementary materials for this article are available online.

中文翻译：

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当确定性近似可用时预测随机计算机模型的输出

摘要 如果没有大量的模拟预算，就很难准确地对随机计算机模型的输出进行统计建模。我们通过利用现成的确定性近似来有效地了解各自的随机计算机模型来缓解这个问题。这是通过两个高斯过程的总和来完成的；一个负责对确定性近似建模，另一个负责使用这种近似来更好地对随机计算机模型进行统计建模。所开发的方法提供了高预测性能并增加了捕获随机计算机模型复杂特征的置信度，即使模拟预算很小。几种合成计算机模型用于概述此方法的功能，并用两个真实世界的例子来展示其实际效用。本文的补充材料可在线获取。