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

Abstract

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.

Authors:
 [1];  [1]; ORCiD logo [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
U.S. Department of Homeland Security; USDOE
OSTI Identifier:
1415409
Report Number(s):
LA-UR-17-25139
Journal ID: ISSN 0169-7439; TRN: US1800807
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Chemometrics and Intelligent Laboratory Systems
Additional Journal Information:
Journal Volume: 175; Journal ID: ISSN 0169-7439
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Inverse prediction; Nuclear forensics

Citation Formats

Lewis, John R., Zhang, Adah, and Anderson-Cook, Christine Michaela. Comparing multiple statistical methods for inverse prediction in nuclear forensics applications. United States: N. p., 2017. Web. doi:10.1016/j.chemolab.2017.10.010.
Lewis, John R., Zhang, Adah, & Anderson-Cook, Christine Michaela. Comparing multiple statistical methods for inverse prediction in nuclear forensics applications. United States. doi:10.1016/j.chemolab.2017.10.010.
Lewis, John R., Zhang, Adah, and Anderson-Cook, Christine Michaela. Sun . "Comparing multiple statistical methods for inverse prediction in nuclear forensics applications". United States. doi:10.1016/j.chemolab.2017.10.010.
@article{osti_1415409,
title = {Comparing multiple statistical methods for inverse prediction in nuclear forensics applications},
author = {Lewis, John R. and Zhang, Adah and Anderson-Cook, Christine Michaela},
abstractNote = {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.},
doi = {10.1016/j.chemolab.2017.10.010},
journal = {Chemometrics and Intelligent Laboratory Systems},
number = ,
volume = 175,
place = {United States},
year = {Sun Oct 29 00:00:00 EDT 2017},
month = {Sun Oct 29 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 29, 2018
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