Bayesian Monte Carlo Evaluation Framework for Imperfect Nuclear Data Article Swipe
Jesse A. Brown
,
Goran Arbanas
,
Andrew Holcomb
,
Dorothea Wiarda
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.13182/t125-36738
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.13182/t125-36738
Bayesian evaluation of resolved resonance region (RRR) nuclear data has historically been carried out using the generalized least squares (GLS) formalism, as implemented in, e.g., SAMMY. We have recently developed a prototype of Bayesian Monte Carlo (BMC) evaluation framework, implemented using a Markov Chain Monte Carlo (MCMC) method with a Metropolis-Hastings (MH) acceptance criterion. This was done in order to remove the approximations underlying the conventional GLS evaluations, namely, the linear approximation, and the approximation that all probability density functions (PDFs) are of the normal kind. Recent works by others have used similar stochastic approaches to quantify cross section uncertainties from ENDF evaluated co-variances, and/or, from integral benchmark data, but those have not been conceived as an evaluation framework like the one presented here.
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- https://www.osti.gov/biblio/1846534
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https://openalex.org/W4225614386Canonical identifier for this work in OpenAlex
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https://doi.org/10.13182/t125-36738Digital Object Identifier
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Bayesian Monte Carlo Evaluation Framework for Imperfect Nuclear DataWork title
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paratextOpenAlex work type
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2024Year of publication
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2024-01-01Full publication date if available
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Jesse A. Brown, Goran Arbanas, Andrew Holcomb, Dorothea WiardaList of authors in order
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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Monte Carlo method, Computer science, Bayesian probability, Imperfect, Data mining, Econometrics, Artificial intelligence, Statistics, Mathematics, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |