Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
提出一种高效的贝叶斯全局敏感性分析方法，用多元高斯过程替代昂贵计算机模型，处理大规模多元数据，在IMPRESSIONS综合评估平台上验证了准确性和效率。
Abstract We apply a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large data sets. The method was applied to multivariate data from the IMPRESSIONS Integrated Assessment Platform version 2. Our empirical results on Integrated Assessment Platform version 2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.