More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability
Abstract
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- April 2022
- DOI:
- 10.48550/arXiv.2204.01623
- arXiv:
- arXiv:2204.01623
- Bibcode:
- 2022arXiv220401623I
- Keywords:
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- Computer Science - Symbolic Computation;
- Computer Science - Machine Learning;
- Mathematics - Algebraic Geometry