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.
翻译:很难在保持预测力的同时找到减少大型模型复杂性的方法,特别是在存在隐蔽参数相互依存关系的情况下。 在这里,我们证明模型偏差分析可以成为战略简化复杂模型的新的宝贵工具。这种分析确定了强力和/或微弱地为模型行为提供信息的参数组合,但这种方法以前没有用来为模型的减少提供信息。我们利用根据实验数据校准的珊瑚计算模型的个案研究,表明模型偏差分析如何从战略角度为维持预测力的简化模型提供信息。此外,在比较分析偏差的各种方法时,我们发现,如果明确确定最合适的模型参数对标准优化程序构成挑战,则贝叶斯方法会有好处。