tailieunhanh - On the use of the BMC to resolve Bayesian inference with nuisance parameters

This paper gives an overview of the evaluation processes used for nuclear data at CEA. After giving Bayesian inference and associated methods used in the CONRAD code [P. Archier et al., Nucl. Data Sheets 118, 488 (2014)], a focus on systematic uncertainties will be given. | On the use of the BMC to resolve Bayesian inference with nuisance parameters EPJ Nuclear Sci. Technol. 4 36 2018 Nuclear Sciences E. Privas et al. published by EDP Sciences 2018 amp Technologies https epjn 2018042 Available online at https REGULAR ARTICLE On the use of the BMC to resolve Bayesian inference with nuisance parameters Edwin Privas Cyrille De Saint Jean and Gilles Noguere CEA DEN Cadarache 13108 Saint Paul les Durance France Received 31 October 2017 Received in final form 23 January 2018 Accepted 7 June 2018 Abstract. Nuclear data are widely used in many research fields. In particular neutron-induced reaction cross sections play a major role in safety and criticality assessment of nuclear technology for existing power reactors and future nuclear systems as in Generation IV. Because both stochastic and deterministic codes are becoming very efficient and accurate with limited bias nuclear data remain the main uncertainty sources. A worldwide effort is done to make improvement on nuclear data knowledge thanks to new experiments and new adjustment methods in the evaluation processes. This paper gives an overview of the evaluation processes used for nuclear data at CEA. After giving Bayesian inference and associated methods used in the CONRAD code P. Archier et al. Nucl. Data Sheets 118 488 2014 a focus on systematic uncertainties will be given. This last can be deal by using marginalization methods during the analysis of differential measurements as well as integral experiments. They have to be taken into account properly in order to give well-estimated uncertainties on adjusted model parameters or multigroup cross sections. In order to give a reference method a new stochastic approach is presented enabling marginalization of nuisance parameters background normalization. . It can be seen as a validation tool but also as a general framework that can be used with any given distribution. An analytic example based on a fictitious .