Abstract

Computer models with both quantitative and qualitative inputs frequently arise in science, engineering and business. Mixed-input Gaussian process models have been used for emulating such models. The key in building this emulator is to accurately estimate the covariance between different categorical levels of the qualitative inputs. This problem is challenging when the number of categorical levels is large. We propose a sparse covariance estimation approach to estimating the covariance matrix with a large number of categorical levels for the mixed-input Gaussian process emulator. The effectiveness of this approach is illustrated with an application of IO operation modes in high performance computing systems.

摘要

具有定量和定性输入的计算机模型经常出现在科学、工程和商业领域。混合输入高斯过程模型已用于模拟此类模型。构建此模拟器的关键是准确估计定性输入的不同分类级别之间的协方差。当分类级别的数量很大时，这个问题很有挑战性。我们提出了一种稀疏协方差估计方法，用于估计混合输入高斯过程模拟器具有大量分类级别的协方差矩阵。这种方法的有效性通过 IO 操作模式在高性能计算系统中的应用来说明。