#include "TFumili.h"
#include "Riostream.h"
#include "TGraphAsymmErrors.h"
#include "TF1.h"
#include "TF2.h"
#include "TF3.h"
#include "TH1.h"
#include "TMath.h"
#include "TROOT.h"
#include "TVirtualFitter.h"
Functions  
void  GraphFitChisquareFumili (Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag) 
Minimization function for Graphs using a Chisquare method. More...  
void  H1FitChisquareFumili (Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag) 
Minimization function for H1s using a Chisquare method. More...  
void  H1FitLikelihoodFumili (Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag) 
Minimization function for H1s using a Likelihood method. More...  
Variables  
TFumili *  gFumili =0 
static const Double_t  gMAXDOUBLE =1e300 
static const Double_t  gMINDOUBLE =1e300 
void GraphFitChisquareFumili  (  Int_t &  npar, 
Double_t *  gin,  
Double_t &  f,  
Double_t *  u,  
Int_t  flag  
) 
Minimization function for Graphs using a Chisquare method.
In case of a TGraphErrors object, ex, the error along x, is projected along the ydirection by calculating the function at the points xexlow and x+exhigh.
The chisquare is computed as the sum of the quantity below at each point:
(y  f(x))**2  ey**2 + (0.5*(exl + exh)*f'(x))**2
where x and y are the point coordinates and f'(x) is the derivative of function f(x). This method to approximate the uncertainty in y because of the errors in x, is called "effective variance" method. The improvement, compared to the previously used method (f(x+ exhigh)  f(xexlow))/2 is of (error of x)**2 order.
NOTE:
In case the function lies below (above) the data point, ey is ey_low (ey_high).
Definition at line 2103 of file TFumili.cxx.
Minimization function for H1s using a Chisquare method.
Definition at line 2049 of file TFumili.cxx.
Minimization function for H1s using a Likelihood method.
Basically, it forms the likelihood by determining the Poisson probability that given a number of entries in a particular bin, the fit would predict it's value. This is then done for each bin, and the sum of the logs is taken as the likelihood. PDF: P=exp(f(x_i))/[F_i]!*(f(x_i))^[F_i] where F_i  experimental value, f(x_i)  expected theoretical value [F_i]  integer part of F_i. drawback is that if F_i>Int_t  GetSumLog will fail for big F_i is faster to use Euler's Gammafunction
Definition at line 2067 of file TFumili.cxx.
TFumili* gFumili =0 
Definition at line 120 of file TFumili.cxx.

static 
Definition at line 123 of file TFumili.cxx.

static 
Definition at line 124 of file TFumili.cxx.