using namespace RooFit ;

void ex10()
{
  // Make an empty workspace that exports its contents to CINT
  RooWorkspace *w = new RooWorkspace("w",kTRUE) ;  

  // This constructs the small variation of the model of Ex1 
  // instead of Nexp = S+B we do Nexp = mu*S + B
  //
  // Here S is now the _nominal_ signal expectation, a constant, and where
  // mu is the 'signal strength modifier', a floating parameters
  // The product mu*S has now the same role as the original S parameter
  // of the model of ex1, but we have expressed the floating parameter
  // in a different way, that is independent of the predicted signal yield
  //
  // mu = 1 --> Signal strength is equal to nominal expection
  // mu = 0 --> Signal srength is zero
  // mu = 2 --> Signal strength is twice the nominal expectation
  //
  // We also choose the nominal values of S and B somewhat differently (20 each)

  w->factory("Poisson::model(N[100],expr::Nexp('mu*S+B',mu[1,0,5],S[20],B[20]))") ;

  RooDataSet data("data","data",*w->var("N")) ;
  w->var("N")->setVal(45) ;
  data.add(*w->var("N")) ;
  
  // Have a look at all the objects created in the workspace
  w->Print("t") ;

  // *** STEP-1 ***
  return ;

  // Now we will transform the _constant_ background with 
  // floating background parameter and introduce a subsidiary
  // measurement that constrains that floating parameter
  
  // First we make B a floating parameter
  w->var("B")->setConstant(kFALSE) ;

  // Now we construct a Gaussian subsidiary measurment
  w->factory("Gaussian::subsidiary(Bnom[20],B,sigmaB[5])") ;

  // Finally, we construct a new model that the product of the
  // original model and the subsidiary measurement
  w->factory("PROD::model2(model,subsidiary)") ;
  
  w->Print("t") ;
  
  
}