Main idea: Create another chapter on the websites for this class or CBE 20258/60258 to cover the following topics.
[ ] Maximum likelihood estimation (MLE) as a lens to explain why weighted nonlinear regression works
[ ] Fisher information matrix (FIM) derived from the MLE perspective with simplifications for i.i.d. Gaussian measurement errors
[ ] Parameter covariance estimate derived from MLE and optimization perspectives. The goal is to explain in two ways when the formulas from CBE 20258 work.
[ ] MLE for (1) proportional plus constant or (2) auto-correlated measurement errors
[ ] Eigendecomposition of FIM for practical identifiability analysis
[ ] Model-based design of experiments using scipy
[ ] Link to ParmEst and Pyomo.DoE tutorials from the summer workshop
Main idea: Create another chapter on the websites for this class or CBE 20258/60258 to cover the following topics.