Parameter Identifiability beyond the Fisher Information Approximation in Data-Driven Modeling
The framework addresses limitations of Fisher Information and profile likelihood in high-dimensional biological modeling by defining 'practical identifiability' based on parameter stability under data perturbations. It introduces a novel metric, Ki, to quantify higher-order identifiability and establishes scaling laws that predict parameter resolvability across different hierarchical scales, providing a robust tool for uncertainty quantification.