使用计算机支持的相关函数的计算机实验的有效模拟器，以及宇宙学的应用
Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology
摘要：
计算机模拟器的统计模拟器已被证明在各种应用中是有用的。广泛采用的仿真器构建模型，使用具有严格正相关函数的高斯过程模型，当仿真器评估的数量很大时，在计算上是难以处理的。我们提出了一个新的模型，它使用低阶回归项和composite支持的相关函数的组合来重新创建所需的预测行为的仿真器在一小部分的计算成本。按照通常的方法，采取的相关性是在每个输入维度的相关性的产品，我们展示了如何对相关性的范围施加限制，提供稀疏性，同时也允许范围相互权衡，从而提供良好的预测性能。我们说明了该方法使用的数据从计算机模拟器的光度红移与20,000模拟器评估和80,000预测。
英文摘要：
Statistical emulators of computer simulators have proven to be useful in a variety of applications. The widely adopted model for emulator building, using a Gaussian process model with strictly positive correlation function, is computationally intractable when the number of simulator evaluations is large. We propose a new model that uses a combination of low-order regression terms and compactly supported correlation functions to recreate the desired predictive behavior of the emulator at a fraction of the computational cost. Following the usual approach of taking the correlation to be a product of correlations in each input dimension, we show how to impose restrictions on the ranges of the correlations, giving sparsity, while also allowing the ranges to trade off against one another, thereby giving good predictive performance. We illustrate the method using data from a computer simulator of photometric redshift with 20,000 simulator evaluations and 80,000 predictions.
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