Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 lor 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.
['Johan Lundberg', 'J. Conrad', 'Wolfgang A. Rolke', 'Angel Lopez']
I am writing a research paper on a idea Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
[{"from": "human", "value": "I am writing a research paper on a idea Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 lor 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."}]