Double_t | BinomialExpP(Double_t sExp, Double_t bExp, Double_t fractionalBUncertainty) |
Double_t | BinomialExpZ(Double_t sExp, Double_t bExp, Double_t fractionalBUncertainty) |
Double_t | BinomialObsP(Double_t nObs, Double_t, Double_t fractionalBUncertainty) |
Double_t | BinomialObsZ(Double_t nObs, Double_t bExp, Double_t fractionalBUncertainty) |
Double_t | BinomialWithTauExpP(Double_t sExp, Double_t bExp, Double_t tau) |
Double_t | BinomialWithTauExpZ(Double_t sExp, Double_t bExp, Double_t tau) |
Double_t | BinomialWithTauObsP(Double_t nObs, Double_t bExp, Double_t tau) |
Double_t | BinomialWithTauObsZ(Double_t nObs, Double_t bExp, Double_t tau) |
Expected P-value for s=0 in a ratio of Poisson means. Here the background and its uncertainty are provided directly and assumed to be from the double Poisson counting setup described in the BinomialWithTau functions. Normally one would know tau directly, but here it is determiend from the background uncertainty. This is not strictly correct, but a useful approximation.
Expected P-value for s=0 in a ratio of Poisson means. Based on two expectations, a main measurement that might have signal and an auxiliarly measurement for the background that is signal free. The expected background in the auxiliary measurement is a factor tau larger than in the main measurement.
P-value for s=0 in a ratio of Poisson means. Here the background and its uncertainty are provided directly and assumed to be from the double Poisson counting setup. Normally one would know tau directly, but here it is determiend from the background uncertainty. This is not strictly correct, but a useful approximation.
P-value for s=0 in a ratio of Poisson means. Based on two observations, a main measurement that might have signal and an auxiliarly measurement for the background that is signal free. The expected background in the auxiliary measurement is a factor tau larger than in the main measurement.
See BinomialExpP
See BinomialObsP