Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Lewis, John R.; Zhang, Adah; Anderson-Cook, Christine Michaela

doi:10.1016/j.chemolab.2017.10.010

Title: Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Journal Article · Sun Oct 29 00:00:00 EDT 2017 · Chemometrics and Intelligent Laboratory Systems

DOI:https://doi.org/10.1016/j.chemolab.2017.10.010· OSTI ID:1415409

Lewis, John R. ^[1]; Zhang, Adah ^[1];

^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors (X) of some underlying causal model producing the observables or responses (Y = g (X) + error). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research in which inverse predictions, along with an assessment of predictive capability, are desired.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: U.S. Department of Homeland Security; USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1415409

Report Number(s):: LA-UR-17-25139; TRN: US1800807

Journal Information:: Chemometrics and Intelligent Laboratory Systems, Vol. 175; ISSN 0169-7439

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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References (17)

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