# More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability

arXiv: https://arxiv.org/abs/2204.01623 · HTML: https://mlsanity.com/paper/2204.01623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Differential elimination for dynamical models via projections with applications to structural identifiability](https://mlsanity.com/paper/2111.00991/) (2021)
- [Global Identifiability of Differential Models](https://mlsanity.com/paper/1801.08112/) (2018)
- [SIAN: software for structural identifiability analysis of ODE models](https://mlsanity.com/paper/1812.10180/) (2018)
- [Web-based Structural Identifiability Analyzer](https://mlsanity.com/paper/2106.15066/) (2021)
- [Computing all identifiable functions of parameters for ODE models](https://mlsanity.com/paper/2004.07774/) (2020)
- [Multi-experiment parameter identifiability of ODEs and model theory](https://mlsanity.com/paper/2011.10868/) (2020)

## Abstract (arXiv)

Structural global parameter identifiability indicates whether one can determine a parameter's value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be infinitely many values, such parameters are called non-identifiable. We present a procedure for accelerating a global identifiability query by eliminating algebraically independent non-identifiable parameters. Our proposed approach significantly improves performance across different computer algebra frameworks.