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Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
Stockholm University, Faculty of Science, Department of Physics.
2010 (English)In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 181, no 3, 683-686 p.Article in journal (Refereed) Published
Abstract [en]

A C++ class was written for the calculation of frequentist confidence intervals using the profile likelihood method. Seven combinations of Binomial, Gaussian, Poissonian and Binomial uncertainties are implemented. The package provides routines for the calculation of upper and lower limits, sensitivity and related properties. It also supports hypothesis tests which take uncertainties into account. It can be used in compiled C++ code, in Python or interactively via the ROOT analysis framework.

Place, publisher, year, edition, pages
2010. Vol. 181, no 3, 683-686 p.
Keyword [en]
Confidence intervals, Hypothesis tests, Systematic uncertainties, Poisson statistics
National Category
Physical Sciences
Research subject
URN: urn:nbn:se:su:diva-50061DOI: 10.1016/j.cpc.2009.11.001ISI: 000274576800021OAI: diva2:382742
authorCount :4Available from: 2011-01-03 Created: 2010-12-21 Last updated: 2011-01-03Bibliographically approved

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Conrad, J.
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