论文标题
通过列子集选择可鲁棒参数可识别性分析
Robust Parameter Identifiability Analysis via Column Subset Selection
论文作者
论文摘要
我们主张一种用于实际参数可识别性分析的数值可靠和准确的方法：将列子集选择（CSS）应用于灵敏度矩阵，而不是计算Fischer信息矩阵的特征值分解。通过CSS的可识别性分析具有三个优点：（i）量化选择为可识别且无法识别的参数子集的可靠性。 （ii）它建立了比较不同算法准确性的标准。 （iii）与应用于Fischer矩阵的特征值方法相比，实现在数值上更准确和可靠，但计算成本没有增加。通过对来自六个物理模型的灵敏度矩阵以及对抗性合成矩阵的灵敏度矩阵进行的广泛数值实验来说明CSS方法的有效性。在CSS方法中，我们建议基于强级别的QR算法实现，因为它具有可识别和不可识别的参数的严格准确性。
We advocate a numerically reliable and accurate approach for practical parameter identifiability analysis: Applying column subset selection (CSS) to the sensitivity matrix, instead of computing an eigenvalue decomposition of the Fischer information matrix. Identifiability analysis via CSS has three advantages: (i) It quantifies reliability of the subsets of parameters selected as identifiable and unidentifiable. (ii) It establishes criteria for comparing the accuracy of different algorithms. (iii) The implementations are numerically more accurate and reliable than eigenvalue methods applied to the Fischer matrix, yet without an increase in computational cost. The effectiveness of the CSS methods is illustrated with extensive numerical experiments on sensitivity matrices from six physical models, as well as on adversarial synthetic matrices. Among the CSS methods, we recommend an implementation based on the strong rank-revealing QR algorithm because of its rigorous accuracy guarantees for both identifiable and non-identifiable parameters.
