  Recent experiments [Phys. Rev. Lett. 123, 092503(2019); Phys. Rev. Lett. 118,
222501 (2017)] have made remarkable progress in measurements of the isotopic
fission-fragment yields of the compound nucleus $^{239}$U, which is of great
interests for fast-neutron reactors and for benchmarks of fission models. We
apply the Bayesian neural network (BNN) approach to learn existing evaluated
charge yields and infer the incomplete charge yields of $^{239}$U. We found the
two-layer BNN is improved compared to the single-layer BNN for the overall
performance. Our results support the normal charge yields of $^{239}$U around
Sn and Mo isotopes. The role of odd-even effects in charge yields has also been
studied.
