  We demonstrate that Bayesian machine learning can be used to treat the vast
amount of experimental fission data which are noisy, incomplete, discrepant,
and correlated. As an example, the two-dimensional cumulative fission yields
(CFY) of neutron-induced fission of $^{238}$U are evaluated with energy
dependencies and uncertainty qualifications. For independent fission yields
(IFY) with very few experimental data, the heterogeneous data fusion of CFY and
IFY is employed to interpolate the energy dependence. This work shows that
Bayesian data fusion can facilitate the further utilization of imperfect raw
nuclear data.
