A general workflow for parameter estimation to help establish confidence in model predictions

Parameter estimation is an important step in model development that helps establish confidence in the model’s predictions. During and after model calibration, several methodologies can be employed to further validate the model and build confidence in its predictions. In this webinar, a general workflow for model calibration and evaluation is presented and executed in SimBiology and MATLAB. Highlights include:
* Using global and local sensitivity analysis to inform model calibration strategy * Assessing structural and practical parameter identifiability * Parameter estimation using local and global optimization techniques * Validation using hold-out data * Uncertainty quantification