Sunday 7 January 2018 photo 14/30
|
Rooabspdf: >> http://eam.cloudz.pw/download?file=rooabspdf << (Download)
Rooabspdf: >> http://eam.cloudz.pw/read?file=rooabspdf << (Read Online)
rooaddpdf
roohistpdf
roodatahist
roonllvar
rooplot
roorealvar
rooargset
rooabsreal
BasicMinimumError > · BasicMinimumParameters > · BasicMinimumSeed > · BasicMinimumState > · Event > · ParamHistFunc · PiecewiseInterpolation · Cartesian3D< double > > · Cartesian3D< Double32_t > >. PyROOT. Rcpp. Rgl. RooAbsAnaConvPdf · RooAbsCachedPdf · RooAbsCachedReal · RooAbsData · RooAbsPdf.
10 Dec 2009 In addition, RooAbsPdf objects do not have a static concept of what // variables are parameters and what variables are dependents (which // need to be integrated over for a correct PDF normalization). // Instead the choice of normalization is always specified each time a // normalized values is requested
RooAbsPdf* p p->plotOn(frame) ;. RooAbsReal::plotOn(f) plot on x integrates over variables (y,z) frame->Draw() ;. // list frame contents frame->Print(“v") ;. RooPlot::frame(088aa410): "A RooPlot of "magic x"". Plotting RooRealVar::x: "magic x". Plot contains 2 object(s). (Options="P") RooHist::gData_plot__x: "Histogram of
30 Jun 2015 RooAbsPdf is the abstract interface for all probability density functions The class provides hybrid analytical/numerical normalization for its implementations, error tracing and a MC generator interface. A minimal implementation of a PDF class derived from RooAbsPdf should overload the evaluate() function.
virtual ~RooAbsPdf() virtual Double_t analyticalIntegralWN(Int_t code, const RooArgSet* normSet, const char* rangeName = "0") const Bool_t canBeExtended() const static TClass* Class() static void clearEvalError() static Bool_t evalError() virtual Double_t expectedEvents(const RooArgSet* nset) const virtual Double_t
The official ROOT repository. Contribute to root development by creating an account on GitHub.
and background PDF are flat (they are constant whatever the actual value of the observable) 00024 RooAbsPdf* sigPdf = new RooPolynomial("sigPdf","signal PDF",*x,RooFit::RooConst(0)); 00025 RooAbsPdf* bkgPdf = new RooPolynomial("bkgPdf","background PDF",*x,RooFit::RooConst(0)); 00026 00027 // S+B model:
A minimal implementation of a PDF class derived from RooAbsPdf // should overload the evaluate() function. This functions should // return PDFs value. // // // [Normalization/Integration] // // Although the normalization of a PDF is an integral part of a // probability density function, normalization is treated separately // in
RooAbsPdf is the abstract interface for all probability density functions The class provides hybrid analytical/numerical normalization for its implementations, error tracing and a MC generator interface. A minimal implementation of a PDF class derived from RooAbsPdf should overload the evaluate() function. This functions
13 Dec 2017 RooAbsPdf* model = w->pdf("background") ;. RooFitResult * newfr = model -> fitTo(*data, Save(true), Range(sr3));. // Plot data and PDF overlaid. RooPlot* xframe = x->frame(Title("Plot in control region SR3")) ; data->plotOn(xframe,Name("data_plot")) ; model->plotOn(xframe,VisualizeError(*newfr, 2, true)
Annons