class AliUnfolding: public TObject

 set unfolding method

 enable the creation of a overflow bin that includes all statistics below the given limit

 set number of skipped bins in regularization

 set number of bins in the input (measured) distribution and in the unfolded distribution

 sets the parameters for chi2 minimization

 sets the parameters for Bayesian unfolding

 set function for unfolding with a fit function

 unfolds with unfolding method fgMethodType

 parameters:
  correlation: response matrix as measured vs. generated
  efficiency:  (optional) efficiency that is applied on the unfolded spectrum, i.e. it has to be in unfolded variables. If 0 no efficiency is applied.
  measured:    the measured spectrum
  initialConditions: (optional) initial conditions for the unfolding. if 0 the measured spectrum is used as initial conditions.
  result:      target for the unfolded result
  check:       depends on the unfolding method, see comments in specific functions

  return code: see UnfoldWithMinuit/UnfoldWithBayesian/UnfoldWithFunction

 fill static variables needed for minuit fit

 implementation of unfolding (internal function)

 unfolds <measured> using response from <correlation> and effiency <efficiency>
 output is in <result>
 <initialConditions> set the initial values for the minimization, if 0 <measured> is used
   negative values in initialConditions mean that the given parameter is fixed to the absolute of the value
 if <check> is true no unfolding is made, instead only the chi2 without unfolding is printed

 returns minuit status (0 = success), (-1 when check was set)

 unfolds a spectrum using the Bayesian method

 homogenity term for minuit fitting
 pure function of the parameters
 prefers constant function (pol0)

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters
 prefers linear function (pol1)

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters
 prefers logarithmic function (log)

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters
 minimizes the total curvature (from Unfolding Methods In High-Energy Physics Experiments,
 V. Blobel (Hamburg U.) . DESY 84/118, Dec 1984. 40pp.

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters
 calculates entropy, from
 The method of reduced cross-entropy (M. Schmelling 1993)

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters
 prefers power law with n = -5

 Does not take into account efficiency

 homogenity term for minuit fitting
 pure function of the parameters
 prefers a powerlaw (linear on a log-log scale)

 The calculation takes into account the efficiencies

 fit function for minuit
 does: (m - Ad)W(m - Ad) where m = measured, A correlation matrix, d = guess, W = covariance matrix

 Draw histograms of
   - Result folded with response
   - Penalty factors
   - Chisquare contributions
 (Useful for debugging/sanity checks and the interactive unfolder)

 If a canvas pointer is given (canv != 0), it will be used for all
 plots; 3 pads are made if needed.

 Helper function for interactive unfolding

 Function to do interactive unfolding
 A canvas is drawn with the unfolding result
 Change the histogram with your mouse and all histograms
 will be updated automatically

 draws residuals of solution suggested by params and effect of regularization

 MvL: THIS MUST BE INCORRECT.
 Need heff to calculate params from TH1 'corrected'

 fill residuals histogram of solution suggested by params and effect of regularization

 draws the penalty factors as function of multiplicity of the current selected regularization

 draws the penalty factors as function of multiplicity of the current selected regularization

 fit function for minuit
 uses the TF1 stored in fgFitFunction

 correct spectrum using minuit chi2 method applying a functional fit

 Finds the first bin where the content is below fgStatLimit and combines all values for this bin and larger bins
 The same limit is applied to the correlation

 unfolds and assigns bias as errors with Eq. 19 of Cowan, "a survey of unfolding methods for particle physics"
 b_i = sum_j dmu_i/dn_j (nu_j - n_j) with nu_j as folded guess, n_j as data
 dmu_i/dn_j is found numerically by changing the bin content and re-unfolding

 return code: 0 (success) -1 (error: from Unfold(...) )