Maximum likelihood estimation with poisson (counting) statistics for waste drum inspection

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Description

This note provides a preliminary look at the issues involved in waste drum inspection when emission levels are so low that central limit theorem arguments do not apply and counting statistics, rather than the usual Gaussian assumption, must be considered. At very high count rates the assumption of Gaussian statistics is reasonable, and the maximum likelihood arguments that we discuss below for low count rates would lead to the usual approach of least squares fits. Least squares is not the the best technique for low counts, and we will develop the maximum likelihood estimators for the low count case.

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19 p.

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Goodman, D. May 1, 1997.

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Description

This note provides a preliminary look at the issues involved in waste drum inspection when emission levels are so low that central limit theorem arguments do not apply and counting statistics, rather than the usual Gaussian assumption, must be considered. At very high count rates the assumption of Gaussian statistics is reasonable, and the maximum likelihood arguments that we discuss below for low count rates would lead to the usual approach of least squares fits. Least squares is not the the best technique for low counts, and we will develop the maximum likelihood estimators for the low count case.

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19 p.

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INIS; OSTI as DE98050898

Other: FDE: PDF; PL:

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  • Other Information: PBD: 1 May 1997

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  • Other: DE98050898
  • Report No.: UCRL-ID--127361
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/632788 | External Link
  • Office of Scientific & Technical Information Report Number: 632788
  • Archival Resource Key: ark:/67531/metadc696835

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Office of Scientific & Technical Information Technical Reports

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  • May 1, 1997

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  • Aug. 14, 2015, 8:43 a.m.

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  • April 10, 2017, 2:31 p.m.

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Goodman, D. Maximum likelihood estimation with poisson (counting) statistics for waste drum inspection, report, May 1, 1997; California. (digital.library.unt.edu/ark:/67531/metadc696835/: accessed April 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.