Dear all,
I would like to know whether all constant terms which do not depend on the fit parameters are considered in the expression of a binned likelihood in Roofit (using RooAbsPdf::createNLL() or RooAbsPdf::fitTo) ?
Let me give you a bit of context. I have a Time-Of-Flight distribution whose generating process depends on many experimental parameters. The distribution presents tails on both side and is Gaussian-like in the center of the distribution. In other words the generating process is unknown and I am faced with the problem of having to choose an adequate fitting pdf, which I also want to be parsimonious. Additionnally, the experimental data are recorded using an MultiChannelAanlyzer whose output is an histogram. Thus, I model my data using a binned likelihood in RooFit using different pdf (Gaussian, Gaussian-Exp convolutions…). I would like to use the nll from RooFit to compute AIC differences between the different models, all within a set of candidate models that I defined a priori, in order to study which one would be closer to the true process generating my data. I would like to avoid having unnecessary fitting parameters in my approximating model used for the analysis. It seems that the AIC approach is well suited for this kind of problems. If the data suggest many models are equally good approximations to the true unknown model I could use AIC weights to average over different models. If someone was already faced with similar situations maybe he/she can also comment on the aforementionned approach?
Thank you very much in advance.
Regards,
MM