{
    // Build Gaussian Pdf with an expected yield
    RooRealVar x("x","my observable",-10,10);
    RooRealVar mean("mean","The Gaussian mean",0,-10,10);
    RooRealVar sigma("sigma","The Gaussian sigma",3,0,5);
    RooGaussian gaus("gaus", "gaussian PDF", x, mean, sigma);

    // Init a standard Root canvas
    TCanvas* mycan=new TCanvas();

    // Plot PDF
    RooPlot* myframe = x.frame();
    gaus.plotOn(myframe);
    myframe->Draw();

    // Generate a Toy MC set
    RooDataSet* data = gaus.generate(x,10000);

    // Fit the pdf to the data (ML)
    gaus.fitTo(*data);

    // Values of the parameters
    mean.Print();
    sigma.Print();

    // Plot data and pdf overlayed
    RooPlot* myframe2 = x.frame();
    data->plotOn(myframe2);
    gaus.plotOn(myframe2);
    TCanvas* mycan2=new TCanvas();mycan2.cd();
    myframe2->Draw();

    // Attach a norm to the pdf
    RooRealVar norm("norm","The normalisation",1000,0,10000);
    RooExtendPdf gausext("gausext","gaussian PDF+normalisation",gaus,norm);

    // Asimov dataset
    RooDataHist* AsimovDataSet = gausext->generateBinned(x,RooFit::ExpectedData());
    RooPlot* myframe3 = x.frame();
    AsimovDataSet->plotOn(myframe3);
    gausext.plotOn(myframe3);
    TCanvas* mycan3=new TCanvas();mycan3.cd();
    myframe3->Draw();      
    }