Uncertainty Quantification and Propagation in Nuclear Density Functional Theory

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better root nuclear DFT in the theory of nuclear forces [see Duguet et al., this issue], energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature. PACS. 21.60.Jz Nuclear Density Functional Theory and extensions – 21.10.-k Properties of nuclei; nuclear energy levels – 02.30.Zz Inverse problems – 02.60.Pn Numerical optimization – 02.70.Uu Applications of