poisson_interval
- coffea.hist.poisson_interval(sumw, sumw2, coverage=0.6826894921370859)[source]
Frequentist coverage interval for Poisson-distributed observations
- Parameters:
sumw (numpy.ndarray) – Sum of weights vector
sumw2 (numpy.ndarray) – Sum weights squared vector
coverage (float, optional) – Central coverage interval, defaults to 68%
Calculates the so-called ‘Garwood’ interval, c.f. https://www.ine.pt/revstat/pdf/rs120203.pdf or http://ms.mcmaster.ca/peter/s743/poissonalpha.html For weighted data, this approximates the observed count by
sumw**2/sumw2, which effectively scales the unweighted poisson interval by the average weight. This may not be the optimal solution: see https://arxiv.org/pdf/1309.1287.pdf for a proper treatment. When a bin is zero, the scale of the nearest nonzero bin is substituted to scale the nominal upper bound. If all bins zero, a warning is generated and interval is set tosumw.