using namespace RooFit;

void MLFitGaussExpo()
{
// Generate a sample of MC events (Gauss+ Exponential background) and perform an extended ML fit to this sample
//Observable x in the range [0,2]
RooRealVar x("x","x",0., 2.) ;

//Gaussian PDF for signal 
RooRealVar xmean("xmean","mean of gaussian ",1., 0., 2.) ; 
RooRealVar xsigma("xsigma","width of gaussian ",0.1, 0., 1.) ; 
RooGaussian gauss("gauss","gaussian PDF", x, xmean, xsigma); 

//Exponential  PDF for background
RooRealVar tau("tau","tau", -1.5, -0., -2.);
RooExponential expo("expo","Exponentxsial", x, tau);

 RooRealVar frac("frac", "fraction", 0.08);
 RooAddPdf model("model", "model", RooArgList(gauss,expo), frac); 


//Generate MC simulated (toyMC) sample)
RooDataSet *data = model.generate(x, 10000);  // simulate a sample of 10000 events

//Perform  Extended ML fit of PDF model to toyMC data
model.fitTo(*data); //ML fit

//Plot Toy data and PDF overlaid
 RooPlot *xframe = x.frame(Title("MC data and PDFs (exp + gauss)"));
data->plotOn(xframe);
model.plotOn(xframe);
model.plotOn(xframe, Components(expo), LineStyle(kDashed));
xframe->Draw();
frac.Print();
}