Hello everyone. I had a small question about uncertainty representation for histograms.

When representing data, I saw that RooFit takes the frequentist approach to calculate uncertainties (with `ROOT::Math::gamma_quantile`

), resulting in asymmetric uncertainties for each bin (as explained page 16 of this presentation).

I was wondering if we could do the same with MC. Usually MC distributions have weights for each events. Is this frequentist approach still valid? If no, what about if each event has the same constant weight?

More generally, can we estimate the new asymmetrical uncertainties of the weighted histogram by dividing the asymmetrical uncertainties of the unweighted histogram by the weights? This works with the `sqrt(N)`

error, why not with the asymmetrical ones?