计算机建模实验中的金块案例
Cases for the nugget in modeling computer experiments
摘要：
大多数用于计算机实验的替代模型都是插值器，最常见的插值器是高斯过程（GP），它故意省略了称为块金的小尺度（测量）误差项。解释是，计算机实验，根据定义，是“确定性的”，因此没有测量误差。我们认为这对于计算机实验来说太狭隘了，而且在统计上也是一种效率低下的建模方法。我们表明，估计（非零）金块可以导致代理模型具有更好的统计特性，如预测精度和覆盖范围，在各种常见的情况下。
英文摘要：
Most surrogate models for computer experiments are interpolators, and the most common interpolator is a Gaussian process (GP) that deliberately omits a small-scale (measurement) error term called the nugget. The explanation is that computer experiments are, by definition, "deterministic", and so there is no measurement error. We think this is too narrow a focus for a computer experiment and a statistically inefficient way to model them. We show that estimating a (non-zero) nugget can lead to surrogate models with better statistical properties, such as predictive accuracy and coverage, in a variety of common situations.
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