  Bayesian neural network (BNN) approach is employed to improve the nuclear
mass predictions of various models. It is found that the noise error in the
likelihood function plays an important role in the predictive performance of
the BNN approach. By including a distribution for the noise error, an
appropriate value can be found automatically in the sampling process, which
optimizes the nuclear mass predictions. Furthermore, two quantities related to
nuclear pairing and shell effects are added to the input layer in addition to
the proton and mass numbers. As a result, the theoretical accuracies are
significantly improved not only for nuclear masses but also for single-nucleon
separation energies. Due to the inclusion of the shell effect, in the unknown
region, the BNN approach predicts a similar shell-correction structure to that
in the known region, e.g., the predictions of underestimation of nuclear mass
around the magic numbers in the relativistic mean-field model. This manifests
that better predictive performance can be achieved if more physical features
are included in the BNN approach.
