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 in SFCOMPO are absent. 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 it compares favorably against results obtained by replacing missing information with constant values.
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Acknowledgments
This manuscript has been authored by Consolidated Nuclear Security, LLC, (CNS) and Oak Ridge National Laboratory (ORNL) under Contract Nos. DE-NA0001942 and DE-AC05-00OR22725, respectively, with the U.S. Department of Energy. The work was performed under an Interagency Agreement with the Department of Homeland Security. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Energy, CNS, or ORNL. The authors are grateful to Dr. Michael Sharp, of the University of Tennessee at Knoxville, for a critical review of the manuscript.
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Langan, R.T., Archibald, R.K. & Lamberti, V.E. Nuclear forensics analysis with missing data. J Radioanal Nucl Chem 308, 687–692 (2016). https://doi.org/10.1007/s10967-015-4458-x
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DOI: https://doi.org/10.1007/s10967-015-4458-x