A new method for a priori practical identifiability.

ABSTRACT:

Background and objective

Practical identifiability analysis, i.e., ascertaining whether a model property can be determined from given data, is central to model-based data analysis in biomedicine. The main approaches used today all require that coverage of the parameter space be exhaustive, which is usually impossible. An alternative could be using structural identifiability methods, since they do not need such coverage. However, current structural methods are unsuited for practical identifiability analysis, since they assume that all higher-order derivatives of the measured variables are available. Herein, we provide new definitions and methods that allow for this assumption to be relaxed.

Methods and results

We introduce the concept of [Formula: see text]-identifiability, w

SUBMITTER: Thompson P 

PROVIDER: S-EPMC12270335 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature