ROOT Statistics Classes
- Calculate the CL upper limit using the Feldman-Cousins method as described
in PRD V57 #7, p3873-3889
- Fits MC fractions to data histogram (a la HMCMLL, R. Barlow and C. Beeston,
Comp. Phys. Comm. 77 (1993) 219-228). Takes into account both data and Monte
Carlo statistical uncertainties through a 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. Some special care needs to be taken
in the case of bins with zero content.
- Algorithm to compute 95% C.L. limits using the Likelihood ratio semi-Bayesian
method; see e.g. T. Junk, NIM A434, p. 435-443, 1999). It
takes signal, background and data histograms wrapped in a TLimitDataSource
as input and runs a set of Monte Carlo experiments in order to compute the
limits. If needed, inputs are fluctuated according to systematics.
- General minimisation
Multi-dim parametrisation and fitting
A Neural Network class
Principal Component Analysis
- Computes confidence intervals for the rate of a Poisson in the presence
of background and efficiency with a fully frequentist treatment of the uncertainties
in the efficiency and background estimate using the profile likelihood method.
The signal is always assumed to be Poisson; background may be Poission, Gaussian,
or user-supplied; efficiency may be Binomial, Gaussian, or user-supplied.
See publication at Nucl.Instrum.Meth.A551:493-503,2005.
1- and 2-dim background estimation, smoothing, deconvolution, peak search
and fitting, and orthogonal transformations
- You can also look at:
TH1 base class for
the histograming package
ntuple manipulation and analysis system
Linear algebra package
TMath small utility
Last update: 25-Nov-2004 by Ilka Antcheva