Watching the chi2 value is the logical thing to do. And if the value
increases between iterations, then it is logical that alarm bells
should ring. Without creating statistics or parameter counts for each
iteration, it can be hard to tell that the increasing chi-squared
value is correlated with a massive collapse of model complexity.
On the other hand making the leap to watching the AIC value, from the
statistical point of view of model selection, I would say is
illogical. It's only if you think of the AIC value as an
approximation to the discrepancy, a measure of lack of fit, that it
then makes sense to follow that value over the iterations. It also
doesn't help that a written description of these concepts is missing
from the 'full_analysis.py' script! Optimising parsimony to obtain
the model-free results is a bit of a new concept.
Edward
Re: Diffusion Tensor - Global correlation time