We introduce statistical techniques required to handle complex computer models with potential applications to astronomy. Computer experiments play a critical role in almost all fields of scientific research and engineering. These computer experiments, or simulators, are often computationally expensive, leading to the use of emulators for rapidly approximating the outcome of the experiment. Gaussian process models, also known as Kriging, are the most common choice of emulator. While emulators offer significant improvements in computation over computer simulators, they require a selection of inputs along with the corresponding outputs of the computer experiment to function well. Thus, it is important to select inputs judiciously for the full computer simulation to construct an accurate emulator. Space-filling designs are efficient when the general response surface of the outcome is unknown, and thus they are a popular choice when selecting simulator inputs for building an emulator. In this tutorial we discuss how to construct these space filling designs, perform the subsequent fitting of the Gaussian process surrogates, and briefly indicate their potential applications to astronomy research.

我们介绍了处理复杂计算机模型所需的统计技术，这些模型具有天文学的潜在应用。计算机实验在几乎所有科学研究和工程领域都发挥着至关重要的作用。这些计算机实验或模拟器通常在计算上很昂贵，导致使用模拟器来快速逼近实验结果。高斯过程模型，也称为克里金法，是最常见的仿真器选择。虽然模拟器在计算方面比计算机模拟器有显着改进，但它们需要选择输入以及计算机实验的相应输出才能正常运行。因此，重要的是要为完整的计算机模拟明智地选择输入以构建准确的仿真器。当结果的一般响应面未知时，空间填充设计是有效的，因此在选择模拟器输入以构建仿真器时，它们是一种流行的选择。在本教程中，我们将讨论如何构建这些空间填充设计，执行高斯过程代理的后续拟合，并简要说明它们在天文学研究中的潜在应用。