This repository contains several Jupyter notebooks with Julia code that are associated with "Parameter identifiability, parameter estimation and model prediction for differential equation models" by Matthew J Simpson and Ruth E Baker.
These worksheets are to be presented at the 2024 SIAM Life Sciences Conference in Portland, Oregon. A write-up of the results are available on arXiv at https://arxiv.org/abs/2405.08177
Newton.ipynb: Jupyter notebook for calculations in Section 2: Modelling with ODEs.
PDEAdditive.ipynb: Jupyter notebook for calculations in Section 3 with addivie Gaussian noise: Modelling with PDEs.
PDELogNormal.ipynb: Jupyter notebook for calculations in Section 3 with multiplicativ log normal noise: Modelling with PDEs.
BVP.ipynb: Jupyter notebook for calculations in Section 4 with the standard model parameterization: Modelling with a BVP: Dealing with non-identifiability.
BVP_Rescaled.ipynb: Jupyter notebook for calculations in Section 4 with thee re-scaled model parameterization: Modelling with a BVP: Dealing with non-identifiability.