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Statistical analysis for nuclear forensics experiments
Author(s) -
AndersonCook Christine M.,
Burr Tom,
Hamada M. S.,
Thomas Edward V.
Publication year - 2015
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11278
Subject(s) - frequentist inference , calibration , computer science , bayesian probability , set (abstract data type) , data mining , statistical analysis , data set , statistics , bayesian inference , artificial intelligence , mathematics , programming language
As with any type of forensics, nuclear forensics seeks to infer historical information using models and data. This article connects nuclear forensics and calibration. We present statistical analyses of a calibration experiment that connect several responses to the associated set of input values and then ‘make a measurement’ using the calibration model. Previous and upcoming real experiments involving production of PuO 2 powder motivate this article. Both frequentist and Bayesian approaches are considered, and we report findings from a simulation study that compares different analysis methods for different underlying responses between inputs and responses, different numbers of responses, different amounts of natural variability, and replicated or non‐replicated calibration experiments and new measurements.