  Fission product yields are key infrastructure data for nuclear applications
in many aspects. It is a challenge both experimentally and theoretically to
obtain accurate and complete energy-dependent fission yields. We apply the
Bayesian neural network (BNN) approach to learn existed fission yields and
predict unknowns with uncertainty quantification. We demonstrated that BNN is
particularly useful for evaluations of fission yields when incomplete
experimental data are available. The BNN results are quite satisfactory on
distribution positions and energy dependencies of fission yields.
