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DOE OSTI · 1908334

Bayesian Monte-Carlo Evaluation Framework of Differential and Integral Data [Slides]

Arbanas, Goran·Holcomb, Andrew·Brown, Jesse·Wiarda, Dorothea
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Abstract

This presentation discusses the Bayesian Monte Carlo evaluation framework of differential and integral data, specifically in relation to two methods that are based on Bayes theorem, which assumes that all PDFs are normal (Gaussian) and that they use a linear approximation for IBEs. The presentation states that basic components of the MC evaluation framework include the computation of random MC ensemble from ENDF File 32, the simulation of IBEs and R-matrix cross section compared to experimental data, and the computation of weighted averages. It also states that the application to U-233 indicates deviation from the conventional linear approximation. In conclusion, evaluation framework will require MCMC method e.g., Metropolis-Hastings (M.-H.).

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73 NUCLEAR PHYSICS AND RADIATION PHYSICS97 MATHEMATICS AND COMPUTINGBayes TheoremEvaluated Nuclear Data File (ENDF)GaussianMetropolis-Hastings (M.-H.)Nuclear Criticality Safety Program (NCSP)Nuclear DataR-Matrix Cross SectionResolved Resonance Range (RRR)

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Arbanas, Goran, Holcomb, Andrew, Brown, Jesse, Wiarda, Dorothea. 2019-11-05. Bayesian Monte-Carlo Evaluation Framework of Differential and Integral Data [Slides]. https://doi.org/10.2172/1908334

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