确定性近似下随机计算机模型输出的预测
Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
摘要：
计算机模型的分析可以通过构建替代模型或仿真器来辅助，这些替代模型或仿真器对数值计算机模型进行统计建模。越来越多的计算机模型变得随机，每次运行时产生不同的输出，即使使用相同的输入值。随机计算机模型更难以分析和模拟-通常需要更多的计算机模型运行来拟合。我们提出了一种方法，使用确定性近似的计算机模型，以更好地构建仿真器。该方法被应用到许多玩具的例子，以及一个理想的流行病学模型，从建筑性能领域的模型。
英文摘要：
The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer models are becoming stochastic, yielding different outputs each time they are run, even if the same input values are used. Stochastic computer models are more difficult to analyse and more difficult to emulate - often requiring substantially more computer model runs to fit. We present a method of using deterministic approximations of the computer model to better construct an emulator. The method is applied to numerous toy examples, as well as an idealistic epidemiology model, and a model from the building performance field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
