Implementation of upper limit calculation for a Poisson variable by Bayesian approach

ZHU Yong-Sheng

doi:10.1088/1674-1137/32/2/010

Chinese Physics C> 2008, Vol. 32> Issue(2) : 126-129 DOI: 10.1088/1674-1137/32/2/010

Implementation of upper limit calculation for a Poisson variable by Bayesian approach

ZHU Yong-Sheng ^,

Institute of High Energy Physics, CAS, Beijing 100049, China

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Abstract：
The calculation of Bayesian confidence upper limit for a Poisson variable including both signal and background with and without systematic uncertainties has been formulated. A Fortran 77 routine, BPULE, has been developed to implement the calculation. The routine can account for systematic uncertainties in the background expectation and signal efficiency. The systematic uncertainties may be separately parameterized by a Gaussian, Log-Gaussian or flat probability density function (pdf). Some technical details of BPULE have been discussed.

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ZHU Yong-Sheng. Implementation of upper limit calculation for a Poisson variable by Bayesian approach[J]. Chinese Physics C, 2008, 32(2): 126-129. doi: 10.1088/1674-1137/32/2/010

ZHU Yong-Sheng. Implementation of upper limit calculation for a Poisson variable by Bayesian approach[J]. Chinese Physics C, 2008, 32(2): 126-129. doi: 10.1088/1674-1137/32/2/010 shu

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Received: 2007-05-16
Revised: 2007-06-12

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Implementation of upper limit calculation for a Poisson variable by Bayesian approach

ZHU Yong-Sheng ^,

Corresponding author: ZHU Yong-Sheng,

Institute of High Energy Physics, CAS, Beijing 100049, China

Received Date: 2007-05-16
Accepted Date: 2007-06-12
Available Online: 2008-02-05

Abstract: The calculation of Bayesian confidence upper limit for a Poisson variable including both signal and background with and without systematic uncertainties has been formulated. A Fortran 77 routine, BPULE, has been developed to implement the calculation. The routine can account for systematic uncertainties in the background expectation and signal efficiency. The systematic uncertainties may be separately parameterized by a Gaussian, Log-Gaussian or flat probability density function (pdf). Some technical details of BPULE have been discussed.