More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability
Symbolic Computation
2022-04-05 v1 Machine Learning
Algebraic Geometry
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.
Cite
@article{arxiv.2204.01623,
title = {More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability},
author = {Ilia Ilmer and Alexey Ovchinnikov and Gleb Pogudin and Pedro Soto},
journal= {arXiv preprint arXiv:2204.01623},
year = {2022}
}