Log of /trunk/hist/hist/inc/TFractionFitter.h
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48324 -
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Modified
Fri Jan 18 10:05:35 2013 UTC (2 years ago) by
moneta
File length: 4491 byte(s)
Diff to
previous 46606
from Gabriel:
- added protection to FindPrediction in case Newton's method fails for whatever reason: maximum number of iterations set at 100000 (and warning message in case this limit is surpassed)
- added protection against negative values in MC observed events input: if no. events is negative an error message is printed
- FindPrediction now caches values from the input histograms instead of repeating the same operations many times
This fixes a problem reported in
http://root.cern.ch/phpBB3/viewtopic.php?f=3&t=15854
Revision
38775 -
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Modified
Fri Apr 8 08:27:27 2011 UTC (3 years, 9 months ago) by
moneta
File length: 4132 byte(s)
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previous 22694
from Frank Filthaut: new version of TFractionFitter providing the possibility top exclude bins from the fit
- Add also an option in the constructor to switch off (option Q) or to turn all (option V) the printout from the fitter
Revision
7519 -
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Modified
Tue Nov 4 22:03:25 2003 UTC (11 years, 2 months ago) by
brun
Original Path:
trunk/hist/inc/TFractionFitter.h
File length: 4023 byte(s)
Diff to
previous 4571
New features added to TFractionFitter by Bram Wijngaarden <dwijngaa@hef.kun.nl>
"I've added some features to the TFractionFitter class:
- the adjusted templates generated by the fit method are now stored and
accessible;
- a Chisquare and NDF have been added.
You can access the adjusted templates using
TH1* GetMCPrediction(Int_t parm) const;
Chi squared and NDF are accessible using
Double_t GetChisquare()
Int_t GetNDF()
Double_t GetProb(),
only if the fit is successful of course (GetChisquare will return 0 if no
successful fit has been performed).
Revision
4571 -
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Added
Mon May 20 15:10:08 2002 UTC (12 years, 8 months ago) by
brun
Original Path:
trunk/hist/inc/TFractionFitter.h
File length: 3390 byte(s)
Add new class TFractionFitter from Frank Filthaut
Fits MC fractions to data histogram (a la HMCMLL, see R. Barlow and C. Beeston,
Comp. Phys. Comm. 77 (1993) 219-228, and http://www.hep.man.ac.uk/~roger/hfrac.f).
The virtue of this fit is that it takes into account both data and Monte Carlo
statistical uncertainties. The way in which this is done is through a standard
likelihood fit using Poisson statistics; however, the template (MC) predictions
are also varied within statistics, leading to additional contributions to the
overall likelihood. This leads to many more fit parameters (one per bin per
template), but the minimisation with respect to these additional parameters is
done analytically rather than introducing them as formal fit parameters.
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