论文标题
模型仿真中的滑坡分析：将数学模型拟合到数据时揭示参数不确定性
Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data
论文作者
论文摘要
这项工作引入了一种全面的方法，以评估模型输出对参数值变化的敏感性，并受到先前信念和数据的组合的约束。这种新颖的方法确定了刚性参数组合强烈影响模型数据拟合的质量，同时揭示了这些关键参数组合中的哪种主要由数据告知或也受到先验的实质影响。我们关注复杂系统中非常常见的环境，与要集体估计的模型参数相比，数据的数量和质量较低，并展示了该技术在生物化学，生态学和心脏电生理学中应用的好处。我们还展示了一旦识别出的刚性参数组合，可以在未来的实验中优先考虑要建模的系统的控制机制，并告知哪些模型参数需要优先考虑集体模型数据拟合的参数推断。
This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This novel approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.
