RobustGaSP: Robust Gaussian Stochastic Process Emulation in R
RobustGaSP: Robust Gaussian Stochastic Process Emulation in R
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DOI：
10.32614/rj-2019-011
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发表时间：
2019-06-01
期刊：
影响因子：
2.1
通讯作者：
Berger, James O.
中科院分区：
文献类型：
--
作者：
Gu, Mengyang;Palomo, Jesus;Berger, James O.
高斯随机过程 (GaSP) 仿真是近似计算密集型计算机模型的强大工具。然而，GaSP 仿真器中的参数估计是一项具有挑战性的任务。没有可用的封闭式估计器，并且标准估计（例如最大似然估计器）会出现许多数值问题。在这个包中，我们实现了一个边际后验模式估计器，用于特殊的先验和参数化。 Gu 等人讨论了这种满足鲁棒参数估计标准的估计方法。 （2018）；其中提供了数学原因来解释为什么稳健的参数估计可以极大地提高仿真器的预测性能。此外，惰性输入（对函数的可变性几乎没有影响的输入）可以从边际后验模式估计中识别出来，而无需额外的计算成本。该软件包还实现了并行部分高斯随机过程 (PP GaSP) 模拟器（Gu 和 Berger (2016)），适用于计算机模型在时空坐标等方面具有多个输出的场景。该软件包可以在默认模式下运行，但也允许多种用户规范，例如指定趋势函数和噪声项的能力。本文研究示例以突出该包在样本外预测方面的性能。
Gaussian stochastic process (GaSP) emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a challenging task. No closed-form estimator is available and many numerical problems arise with standard estimates, e.g., the maximum likelihood estimator. In this package, we implement a marginal posterior mode estimator, for special priors and parameterizations. This estimation method that meets the robust parameter estimation criteria was discussed in Gu et al. (2018); mathematical reasons are provided therein to explain why robust parameter estimation can greatly improve predictive performance of the emulator. In addition, inert inputs (inputs that almost have no effect on the variability of a function) can be identified from the marginal posterior mode estimation at no extra computational cost. The package also implements the parallel partial Gaussian stochastic process (PP GaSP) emulator (Gu and Berger (2016)) for the scenario where the computer model has multiple outputs on, for example, spatial-temporal coordinates. The package can be operated in a default mode, but also allows numerous user specifications, such as the capability of specifying trend functions and noise terms. Examples are studied herein to highlight the performance of the package in terms of out-of-sample prediction.