Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
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DOI：
10.1080/10618600.2020.1750416
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发表时间：
2020-05-07
影响因子：
2.4
通讯作者：
Eames, Matt
中科院分区：
文献类型：
--
作者：
Baker, Evan;Challenor, Peter;Eames, Matt
如果没有大量的模拟预算，对随机计算机模型的输出进行统计建模是很难做到准确的。我们通过利用现成的确定性近似来有效地学习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和来完成的；一个负责模拟确定性近似，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信心，即使在模拟预算很小的情况下也是如此。几个综合计算机模型被用来概述该方法的能力，并使用两个现实世界的例子来展示其实际效用。这篇文章可以在网上找到。
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. for this article are available online.