AIC and BIC are only really valid when comparing nested models anyway.
Re:One parameter is always enough [pdf]
by gammarator on Saturday May 26, 2018 @11:04PM
(#17165256)
This article at least claims that's not right: https://robjhyndman.com/hyndsight/aic/
(And Wikipedia: https://en.m.wikipedia.org/wiki/Akaike_information_criterion...)
Do you have a reference? Or were you thinking of the Likelihood Ratio Test?
Okay reading, it sounds like before Burnham and Anderson (2002) it was only known to be valid for nested models, but they were able to derive it from more fundamental assumptions to remove that restriction.
https://en.wikipedia.org/wiki/Akaike_information_criterion#H...
I wasn't aware of that update until now (despite being 16 years old), thank you!!
The posterior likelihood function has very interesting values for this logistic map fit.
This is on the level of "gotcha". Usually the AIC and BIC are supposed to be negative for a correct model and the for is supposed to have a quantifiable information loss.