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Title: Nuclear Forensics Analysis with Missing and Uncertain Data

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:
 [1] ;  [1] ;  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Y-12 National Security Complex, Oak Ridge, TN (United States)
Publication Date:
OSTI Identifier:
1286883
Grant/Contract Number:
AC05-00OR22725
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
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE
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