2 citations · 2 across the 2 of their papers we have counts for
3 papers
physics.data-an2020
Iterative Bayesian Monte Carlo for nuclear data evaluation
E. Alhassan, D. Rochman, A. Vasiliev +4
In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM) procedure for nuclear data evaluation within a Talys Evaluated Nuclear data Library (TENDL) framework. I…
nucl-th2019★ 2 cited
In search of the best nuclear data file for proton induced reactions: varying both models and their parameters
E. Alhassan, D. Rochman, A. Vasiliev +4
A lot of research work has been carried out in fine tuning model parameters to reproduce experimental data for neutron induced reactions. This however is not the case for proton in…
nucl-th2019
Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo
E. Alhassan, D. Rochman, H. Sjöstrand +3
In this work, a method is proposed for combining differential and integral benchmark experimental data within a Bayesian framework for nuclear data adjustments and multi-level unce…