![]() ![]() This parallelization process is transparent to the user. Since likelihood evaluations are potentially time-consuming, RooFit allows likelihood to be calculated in parallel on multiple processes. The likelihood function behaves like a regular RooFit function and can be drawn in the same way as PDFs RooPlot * frame = myparam. Given a PDF and a data set, a likelihood function can be constructed as: RooAbsReal * nll = pdf. Observable with lower and upper bound: RooRealVar mes ( "mes", "m_ Working with likelihood functions and profile likelihood one object representing a Gaussian PDF.three objects representing the observable, the mean and the sigma parameters,.Mathematical conceptĪ Gaussian PDF consists typically of four objects: A feature of this design philosophy is that all RooFit models always consist of multiple objects. ![]() RooFit introduces a granular structure in its mapping of mathematical data models components to C++ objects: instead of aiming for a monolithic entity describing a data model, each mathematical symbol is represented by a separate object. They allow construction of higher dimensional PDFs out of lower dimensional building block with an intuitive language to introduce and describe correlations between observables.Īnd they also allow the universal implementation of toy Monte Carlo sampling techniques, and are of course an prerequisite for the use of (un-binned) maximum likelihood parameter estimation technique. The defining properties of PDFs, unit normalization with respect to all observables and positive definiteness, also provide important benefits for the design of a structured modeling language: PDFs are easily added with intuitive interpretation of fraction coefficients. The natural modeling language for such distributions are probability density functions (PDF) F(x p) that describe the probability density of the distribution of the observables x in terms of function in parameter p. Experiments of this nature result in data sets obeying Poisson (or binomial) statistics. The core functionality of RooFit is to enable the modeling of ‘event data’ distributions, where each event is a discrete occurrence in time, and has one or more measured observables associated with it. They contain in-depth information about RooFit.ĭiscuss RooFit and RooStats in the ROOT forum. → RooFit tutorialsįor RooFit, Topical Manuals are available at Topical Manuals - RooFit. ![]() Models can be used to perform unbinned maximum likelihood fits, create plots, and generate “toy Monte Carlo” samples for various studies. ROOT provides with the RooFit library a toolkit for modeling the expected distribution of events in a physics analysis.
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