This procedure needs more effort but enables you to re-use the filled object in cases where you want to change one or more weights. It is necessary to create one TEfficiency object for each weight if you investigate a process involving different weights. However, you can assign a global weight to each TEfficiency object ( TEfficiency::SetWeight). Therefore a filling with weights is not possible. Then the efficiency, as well as its upper and lower error, can be calculated for each bin individually.Īs the efficiency can be regarded as a parameter of a binomial distribution, the number of passed and total events must always be integer numbers. The number of passed and total events is therefore stored internally in two histograms ( TEfficiency::fTotalHistogram and TEfficiency::fPassedHistogram). One is usually interested in the dependency of the efficiency on other (binned) variables. It provides several statistical methods for calculating frequentist and Bayesian confidence intervals as well as a function for combining several efficiencies.Įfficiencies have a lot of applications and meanings but in principle, they can be described by the fraction of good/passed events k out of sample containing N events. This class handles the calculation of efficiencies and their uncertainties. VI.1 Information about the internal histograms.Merging and combining TEfficiency objects IV.1 Coverage probabilities for different methods.
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