Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
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DOI：
10.1093/jrsssc/qlad032
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
O. Oyebamiji;C. Nemeth;P. Harrison;R. Dunford;G. Cojocaru
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作者：
O. Oyebamiji;C. Nemeth;P. Harrison;R. Dunford;G. Cojocaru
本文提出了一种新的贝叶斯全局敏感性分析方法。一个多元高斯过程被用来作为代理模型，以取代昂贵的计算机模型。为了提高模型的计算效率和性能，使用了完备的相关函数。目标是生成稀疏矩阵，这在处理大型数据集时具有至关重要的优势。该方法被应用到多变量数据的印象综合评估平台版本2。综合评估平台第2版数据的实证结果表明，所提出的方法是有效的，准确的复杂模型的全局敏感性分析。
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.