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. To supply the application needs, the two-dimensional cumulative fission yields (CFY) of neutron-induced fission of ${}^{238}\mathrm{U}$ are evaluated for energy dependencies and uncertainty qualifications by cross-experiment data fusion. 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 maximum utilization of imperfect raw nuclear data.

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• Fission
• A ≥ 220
• Bayesian methods
• Machine learning
• Nuclear data analysis & compilation