Why is AIC better to use than ML to determine the process model. Is it because it decreases variance, due to favouring models with fewer free parameters??
Yes, log-likelihood scores (the ML optimality criterion) tend to improve as the model increases in complexity – so using this measure alone would lead one to always choose the most complex model. Although this might be wise for Bayesian analysis, for ML analysis it is definitely preferable to use a model that is complex enough but not overly complex and the AIC helps one choose this model.
