Background Data Uncertainty Analysis
Background Data Uncertainty Analysis is a systematic methodological framework within background systems research that characterizes and quantifies the epistemic and aleatoric uncertainties inherent in background data sources, distinguishing it from other analytical approaches by its exclusive focus on the propagation and mitigation of data-derived uncertainties in contextually embedded computational models.
15
Publications
546
Citations
47
Authors
37
Institutions
Publications per year
2017–2026
6
Parents
15
Handling uncertainties in background shapes: the discrete profiling method
Paul Dauncey, M. Kenzie, N. Wardle et al. · Journal of Instrumentation · 2015 · 98 citations · Full text
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47
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37

Johns Hopkins University
Baltimore, United States
2 h-index
1 publications
18 citations
Arizona State University
Tempe, United States
2 h-index
2 publications
36 citations
University of Maryland, College Park
College Park, United States
2 h-index
2 publications
36 citations
Goddard Space Flight Center
Greenbelt, United States
2 h-index
1 publications
18 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
8 publications
269 citations
University of California, Berkeley
Berkeley, United States
2 h-index
1 publications
18 citations
Laboratoire d'Annecy de Physique Théorique
Annecy, France
1 h-index
3 publications
174 citations

Sorbonne Université
Paris, France
1 h-index
1 publications
19 citations
1–8 of 37
JINST · 2 publications · 111 citations
2
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2 publications · 135 citations
2
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JCAP · 2 publications · 73 citations
2
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1 publications · 19 citations
1
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LCA · 1 publications · 23 citations
1
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