message: Add notes on unfolding normalization
  Credit to Markus Fasel and Leticia Cunqueiro for clear explanations of
  these questions.
diff: __Page under construction__
  +Unfolding is generally performed via RooUnfold.
  +
  +# FAQ
  +
  +1. Do I need to normalize the response before passing to RooUnfold?
  +
  + No. RooUnfold will normalize the response when appropriate for the given method. For 1D Bayes, the response is normalized via the code
  + block listed below (either in the `RooUnfoldBayes::train(...)` or `RooUnfoldBayes::unfold(...)`, depending on the particular version.
  +
  + ~~~{.cxx}
  + TMatrixD PEjCi(_ne,_nc), PEjCiEff(_ne,_nc);
  + for (Int_t i = 0 ; i < _nc ; i++) {
  + if (_nCi[i] <= 0.0) { _efficiencyCi[i] = 0.0; continue; }
  + Double_t eff = 0.0;
  + for (Int_t j = 0 ; j < _ne ; j++) {
  + Double_t response = _Nji(j,i) / _nCi[i];
  + PEjCi(j,i) = PEjCiEff(j,i) = response; // efficiency of detecting the cause Ci in Effect Ej
  + eff += response;
  + }
  + _efficiencyCi[i] = eff;
  + Double_t effinv = eff > 0.0 ? 1.0/eff : 0.0; // reset PEjCiEff if eff=0
  + for (Int_t j = 0 ; j < _ne ; j++) PEjCiEff(j,i) *= effinv;
  + }
  + ~~~
  +
  + For SVD, the response should not be normalized.
  +
  +2. Do I need to explicitly correct for the kinematic efficiency?
  +
  + If you provide a fully efficient prior as a separate 1D histogram to the RooUnfoldResponse, then you don't need to correct for the kinematic
  + efficiency. RooUnfold will take care of it. However, if you don't provide the fully efficient prior (or you are in the 2D case where you
  + cannot provide it), then you do need to correct for the kinematic efficiency after unfolding.
  +
  */
  