论文标题
通过分析模型降低战略模型的降低：珊瑚钙化中的案例研究
Strategic model reduction by analysing model sloppiness: a case study in coral calcification
论文作者
论文摘要
在维持预测能力的同时，尤其是在存在隐藏的参数相互依赖的情况下，很难确定减少大型模型的复杂性的方法。在这里，我们证明了模型滑坡的分析可能是策略性地简化复杂模型的新宝贵工具。这样的分析确定了强烈和/或弱信息模型行为的参数组合，但该方法以前尚未用于为模型减少提供信息。使用对实验数据校准的珊瑚钙化模型的案例研究，我们展示了模型滑坡的分析如何从战略上为维持预测能力的模型简化提供信息。此外，在比较分析斜率的各种方法时，我们发现，当明确识别最佳拟合模型参数是标准优化过程的挑战时，贝叶斯方法可能是有利的。
It can be difficult to identify ways to reduce the complexity of large models whilst maintaining predictive power, particularly where there are hidden parameter interdependencies. Here, we demonstrate that the analysis of model sloppiness can be a new invaluable tool for strategically simplifying complex models. Such an analysis identifies parameter combinations which strongly and/or weakly inform model behaviours, yet the approach has not previously been used to inform model reduction. Using a case study on a coral calcification model calibrated to experimental data, we show how the analysis of model sloppiness can strategically inform model simplifications which maintain predictive power. Additionally, when comparing various approaches to analysing sloppiness, we find that Bayesian methods can be advantageous when unambiguous identification of the best-fit model parameters is a challenge for standard optimisation procedures.
