Model Calibration - zward/Amua GitHub Wiki

Model calibration usually involves comparing the model predictions to empirical data to find parameter sets that achieve a good fit.

This is an area of ongoing development in Amua. Right now, there are two exploratory methods available:

• Random: Samples parameter sets at random and evaluates the calibration score for each set
• Approximate Bayesian Computation (ABC): Generates samples from the posterior using the rejection sampling ABC algorithm.

More calibration methods will be added soon!