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FitUtils.hh
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#ifndef NPSTAT_FITUTILS_HH_
#define NPSTAT_FITUTILS_HH_
/*!
// \file FitUtils.hh
//
// \brief Utility functions which facilitate fitting of parametric distributions
//
// Author: I. Volobouev
//
// September 2010
*/
#include "npstat/stat/HistoND.hh"
#include "npstat/stat/AbsDistribution1D.hh"
namespace npstat {
/**
// This function calculates log-likelihood of a 1-d histogram
// fit by a 1-d density (the value can then be maximized -- see,
// for example, MinuitDensityFitFcn1D.hh header in the "interfaces"
// section).
//
// The length of the "binMask" array should be the same as the number
// of bins in the histogram. Only those bins will be used for which
// corresponding mask values are not 0.
//
// The "workBuffer" array should be at least as long as the number
// of bins in the histogram.
//
// "minlog" is the minimal value of log-likelihood which can be
// contributed by one data point (that is, by a part of the bin height
// which corresponds to 1 point). This should normally be a negative
// number with reasonably large magnitude.
//
// The code essentially assumes Poisson distribution of counts
// for each bin, constrained by the requirement that the sum of
// expected events should be equal to the sum of events observed
// (all of this is relevant for unmasked bins only). Note that,
// although histogram scaling will not affect the central value
// of the fit, it will affect the fit error determination.
// Therefore, it is important to use histograms with actual
// event counts rather than any kind of a scaled version.
*/
template<typename Numeric>
double densityFitLogLikelihood1D(const HistoND<Numeric>& histo,
const unsigned char* binMask,
unsigned maskLength,
const AbsDistribution1D& density,
double minlog,
double* workBuffer,
unsigned workBufferLength);
}
#include "npstat/stat/FitUtils.icc"
#endif // NPSTAT_FITUTILS_HH_

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