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Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>13/06/2023
<mark>Journal</mark>Journal of the Royal Statistical Society: Series C (Applied Statistics)
Issue number3
Number of pages39
Pages (from-to)770-808
Publication StatusPublished
Early online date15/05/23
<mark>Original language</mark>English


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.