论文标题
敏感性分析的差异措施
Discrepancy measures for sensitivity analysis
论文作者
论文摘要
尽管灵敏度分析提高了数学模型的透明度和可靠性，但建模者的吸收仍然很少。这部分用其技术要求来解释，非专家可能很难理解和实施。在这里，我们根据差异的概念提出了一种灵敏度分析方法，该方法与输入输出散点图的目视检查一样容易理解。首先，我们表明某些差异度量能够对模型的最具影响力参数进行排名，几乎与基于方差的总灵敏度指数一样准确。然后，我们引入了一种ERSATZ-DISCEPANCY，其敏感性度量与表现最佳的差异算法相匹配，该算法易于实现，更易于解释，并且更快地解释和数量级。
While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the non-specialist. Here we propose a sensitivity analysis approach based on the concept of discrepancy that is as easy to understand as the visual inspection of input-output scatterplots. Firstly, we show that some discrepancy measures are able to rank the most influential parameters of a model almost as accurately as the variance-based total sensitivity index. We then introduce an ersatz-discrepancy whose performance as a sensitivity measure matches that of the best-performing discrepancy algorithms, is simple to implement, easier to interpret and orders of magnitude faster.
