论文标题
大规模气候影响和适应模型的多元灵敏度分析
Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
论文作者
论文摘要
我们为大规模多元数据开发了一种新的有效方法，用于贝叶斯全球灵敏度分析。重点放在具有相关变量的计算要求的模型上。多元高斯过程用作替代昂贵的计算机模型的替代模型。为了提高模型的计算效率和性能，使用了紧凑的相关功能。目的是生成稀疏的矩阵，在处理大型数据集时，它们在使用交叉验证来确定最佳稀疏度时具有至关重要的优势。该方法与强大的自适应大都市算法结合使用，并与平行实现相结合，以加快收敛到目标分布的速度。该方法应用于Impress Integrated评估平台（IAP2）的多元数据集，这是Climsave IAP的扩展，该数据已广泛应用于气候变化影响，适应性和脆弱性评估中。我们对合成和IAP2数据的经验结果表明，所提出的方法对于复杂模型的全球灵敏度分析是有效且准确的。
We develop a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. The focus is on computationally demanding models with correlated variables. 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 datasets, where we use cross-validation to determine the optimal degree of sparsity. This method was combined with a robust adaptive Metropolis algorithm coupled with a parallel implementation to speed up the convergence to the target distribution. The method was applied to a multivariate dataset from the IMPRESSIONS Integrated Assessment Platform (IAP2), an extension of the CLIMSAVE IAP, which has been widely applied in climate change impact, adaptation and vulnerability assessments. Our empirical results on synthetic and IAP2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.
