This is very likely a basic question, but I have not really found any clues on how to perform this efficiently with RooFit.
I have something like 40 datasamples to fit with 4 parameters each, plus 1 parameter correlated across all samples. Right now, I have fits failing when throwing toys because I am performing one simultaneous fit with 160+1 dimensions. I am not surprised with the results at all.
What I would like to be doing is to take the full model and pre-condition parts of it by performing (partial) fits of the each of the 40 datasets one at a time. Once the pre-conditioning is done, the chances for the global fit to converge should be, well, much larger.
Is there a straightforward way to do this in RooFit?