Nuclear Forensics Analysis with Missing and Uncertain Data

Langan, Roisin T.; Archibald, Richard K.; Lamberti, Vincent

doi:10.1007/s10967-015-4458-x

Title: Nuclear Forensics Analysis with Missing and Uncertain Data

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Abstract

We have applied a new imputation-based method for analyzing incomplete data, called Monte Carlo Bayesian Database Generation (MCBDG), to the Spent Fuel Isotopic Composition (SFCOMPO) database. About 60% of the entries are absent for SFCOMPO. The method estimates missing values of a property from a probability distribution created from the existing data for the property, and then generates multiple instances of the completed database for training a machine learning algorithm. Uncertainty in the data is represented by an empirical or an assumed error distribution. The method makes few assumptions about the underlying data, and compares favorably against results obtained by replacing missing information with constant values.

Authors:

Langan, Roisin T. ^[1]; Archibald, Richard K. ^[1]; Lamberti, Vincent ^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Y-12 National Security Complex, Oak Ridge, TN (United States)

Publication Date:: Mon Oct 05 00:00:00 EDT 2015

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1286883

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Radioanalytical and Nuclear Chemistry

Additional Journal Information:: Journal Volume: 1; Journal Issue: 1; Journal ID: ISSN 0236-5731

Publisher:: Springer

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Nuclear forensics; Missing data; Machine learning; Bayesian methods; Monte Carlo methods; Spent fuel isotopic composition (SFCOMPO) database

Citation Formats


                    Langan, Roisin T., Archibald, Richard K., and Lamberti, Vincent. Nuclear Forensics Analysis with Missing and Uncertain Data.  United States: N. p., 2015. 
Web.  doi:10.1007/s10967-015-4458-x.

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                    Langan, Roisin T., Archibald, Richard K., & Lamberti, Vincent. Nuclear Forensics Analysis with Missing and Uncertain Data.  United States.  https://doi.org/10.1007/s10967-015-4458-x

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                    Langan, Roisin T., Archibald, Richard K., and Lamberti, Vincent. Mon .  
"Nuclear Forensics Analysis with Missing and Uncertain Data".  United States.  https://doi.org/10.1007/s10967-015-4458-x.  https://www.osti.gov/servlets/purl/1286883.

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@article{osti_1286883,

  title        = {Nuclear Forensics Analysis with Missing and Uncertain Data},

  author       = {Langan, Roisin T. and Archibald, Richard K. and Lamberti, Vincent},

  abstractNote = {We have applied a new imputation-based method for analyzing incomplete data, called Monte Carlo Bayesian Database Generation (MCBDG), to the Spent Fuel Isotopic Composition (SFCOMPO) database. About 60% of the entries are absent for SFCOMPO. The method estimates missing values of a property from a probability distribution created from the existing data for the property, and then generates multiple instances of the completed database for training a machine learning algorithm. Uncertainty in the data is represented by an empirical or an assumed error distribution. The method makes few assumptions about the underlying data, and compares favorably against results obtained by replacing missing information with constant values.},

  doi          = {10.1007/s10967-015-4458-x},

  journal      = {Journal of Radioanalytical and Nuclear Chemistry},

  number       = 1,

  volume       = 1,

  place        = {United States},

  year         = {Mon Oct 05 00:00:00 EDT 2015},

  month        = {Mon Oct 05 00:00:00 EDT 2015}

}

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