<commit_before>/////////////////////////////////////////////////////////////////////////
//
// 'Bayesian Calculator' RooStats tutorial macro #701
// author: <NAME>
// date Sep 2009
//
// This tutorial shows an example of using the BayesianCalculator class 
//
/////////////////////////////////////////////////////////////////////////


#include "RooRealVar.h"
#include "RooWorkspace.h"
#include "RooDataSet.h"
#include "RooPlot.h"
#include "RooMsgService.h"

#include "RooStats/BayesianCalculator.h"
#include "RooStats/SimpleInterval.h"
#include "TCanvas.h"

using namespace RooFit;
using namespace RooStats;

void rs701_BayesianCalculator(bool useBkg = true, double confLevel = 0.90)
{


  RooWorkspace* w = new RooWorkspace("w",true);
  w->factory("SUM::pdf(s[0,15]*Uniform(x[0,1]),b[1,0,2]*Uniform(x))");
  w->factory("Gaussian::prior_b(b,1,1)");
  w->factory("PROD::model(pdf,prior_b)");
  RooAbsPdf* model = w->pdf("model");  // pdf*priorNuisance
  RooArgSet nuisanceParameters(*(w->var("b")));
  


  RooAbsRealLValue* POI = w->var("s");
  RooAbsPdf* priorPOI  = (RooAbsPdf *) w->factory("Uniform::priorPOI(s)");  
  RooAbsPdf* priorPOI2 = (RooAbsPdf *) w->factory("GenericPdf::priorPOI2('1/s',s)");

  w->factory("n[3]"); // observed number of events
  // create a data set with n observed events
  RooDataSet data("data","",RooArgSet(*(w->var("x")),*(w->var("n"))),"n");
  data.add(RooArgSet(*(w->var("x"))),w->var("n")->getVal());

  // to suppress messgaes when pdf goes to zero
  RooMsgService::instance().setGlobalKillBelow(RooFit::FATAL) ;

  RooArgSet * nuisPar = 0;
  if (useBkg) nuisPar = &nuisanceParameters;
  //if (!useBkg) ((RooRealVar *)w->var("b"))->setVal(0);

  double size = 1.-confLevel;
  std::cout << "\nBayesian Result using a Flat prior " << std::endl;
  BayesianCalculator bcalc(data,*model,RooArgSet(*POI),*priorPOI, nuisPar);
  bcalc.SetTestSize(size);
  SimpleInterval* interval = bcalc.GetInterval();
  double cl = bcalc.ConfidenceLevel();
  std::cout << cl <<"% CL central interval: [ " << interval->LowerLimit() << " - " << interval->UpperLimit() 
            << " ] or " 
            << cl+(1.-cl)/2 << "% CL limits\n";
  RooPlot * plot = bcalc.GetPosteriorPlot();
  TCanvas * c1 = new TCanvas("c1","Bayesian Calculator Result");
  c1->Divide(1,2); 
  c1->cd(1);
  plot->Draw();

  // don;t run with background events - will take too long
  std::cout << "\nBayesian Result using a 1/s prior (no background) " << std::endl;
  BayesianCalculator bcalc2(data,*model,RooArgSet(*POI),*priorPOI2);
  bcalc2.SetTestSize(size);
  SimpleInterval* interval2 = bcalc2.GetInterval();
  cl = bcalc2.ConfidenceLevel();
  std::cout << cl <<"% CL central interval: [ " << interval2->LowerLimit() << " - " << interval2->UpperLimit() 
            << " ] or " 
            << cl+(1.-cl)/2 << "% CL limits\n";

  RooPlot * plot2 = bcalc2.GetPosteriorPlot();
  c1->cd(2);
  plot2->Draw();
  c1->Update();
  
  // observe one event while expecting one background event -> the 95% CL upper limit on s is 4.10
  // observe one event while expecting zero background event -> the 95% CL upper limit on s is 4.74
}
<commit_msg>fix a problem with runniong 1/s prior. Use now also a 1/sqrt(s) prior<commit_after>/////////////////////////////////////////////////////////////////////////
//
// 'Bayesian Calculator' RooStats tutorial macro #701
// author: <NAME>
// date Sep 2009
//
// This tutorial shows an example of using the BayesianCalculator class 
//
/////////////////////////////////////////////////////////////////////////


#include "RooRealVar.h"
#include "RooWorkspace.h"
#include "RooDataSet.h"
#include "RooPlot.h"
#include "RooMsgService.h"

#include "RooStats/BayesianCalculator.h"
#include "RooStats/SimpleInterval.h"
#include "TCanvas.h"

using namespace RooFit;
using namespace RooStats;

void rs701_BayesianCalculator(bool useBkg = true, double confLevel = 0.90)
{


  RooWorkspace* w = new RooWorkspace("w",true);
  w->factory("SUM::pdf(s[0.001,15]*Uniform(x[0,1]),b[1,0,2]*Uniform(x))");
  w->factory("Gaussian::prior_b(b,1,1)");
  w->factory("PROD::model(pdf,prior_b)");
  RooAbsPdf* model = w->pdf("model");  // pdf*priorNuisance
  RooArgSet nuisanceParameters(*(w->var("b")));
  


  RooAbsRealLValue* POI = w->var("s");
  RooAbsPdf* priorPOI  = (RooAbsPdf *) w->factory("Uniform::priorPOI(s)");  
  RooAbsPdf* priorPOI2 = (RooAbsPdf *) w->factory("GenericPdf::priorPOI2('1/sqrt(@0)',s)");

  w->factory("n[3]"); // observed number of events
  // create a data set with n observed events
  RooDataSet data("data","",RooArgSet(*(w->var("x")),*(w->var("n"))),"n");
  data.add(RooArgSet(*(w->var("x"))),w->var("n")->getVal());

  // to suppress messgaes when pdf goes to zero
  RooMsgService::instance().setGlobalKillBelow(RooFit::FATAL) ;

  RooArgSet * nuisPar = 0;
  if (useBkg) nuisPar = &nuisanceParameters;
  //if (!useBkg) ((RooRealVar *)w->var("b"))->setVal(0);

  double size = 1.-confLevel;
  std::cout << "\nBayesian Result using a Flat prior " << std::endl;
  BayesianCalculator bcalc(data,*model,RooArgSet(*POI),*priorPOI, nuisPar);
  bcalc.SetTestSize(size);
  SimpleInterval* interval = bcalc.GetInterval();
  double cl = bcalc.ConfidenceLevel();
  std::cout << cl <<"% CL central interval: [ " << interval->LowerLimit() << " - " << interval->UpperLimit() 
            << " ] or " 
            << cl+(1.-cl)/2 << "% CL limits\n";
  RooPlot * plot = bcalc.GetPosteriorPlot();
  TCanvas * c1 = new TCanvas("c1","Bayesian Calculator Result");
  c1->Divide(1,2); 
  c1->cd(1);
  plot->Draw();
  c1->Update();

  std::cout << "\nBayesian Result using a 1/sqrt(s) prior  " << std::endl;
  BayesianCalculator bcalc2(data,*model,RooArgSet(*POI),*priorPOI2,nuisPar);
  bcalc2.SetTestSize(size);
  SimpleInterval* interval2 = bcalc2.GetInterval();
  cl = bcalc2.ConfidenceLevel();
  std::cout << cl <<"% CL central interval: [ " << interval2->LowerLimit() << " - " << interval2->UpperLimit() 
            << " ] or " 
            << cl+(1.-cl)/2 << "% CL limits\n";

  RooPlot * plot2 = bcalc2.GetPosteriorPlot();
  c1->cd(2);
  plot2->Draw();
  gPad->SetLogy();
  c1->Update();
  
  // observe one event while expecting one background event -> the 95% CL upper limit on s is 4.10
  // observe one event while expecting zero background event -> the 95% CL upper limit on s is 4.74
}

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