  The Bayesian machine learning is a promising tool for the evaluation of
nuclear fission data but its potential capability has not been fully realized.
We attempt to optimize the performances of the multilayer Bayesian neural
networks for evaluations of fission yields. The influences of adjustments of
learning data, activation functions, network structures have been studied. In
particular, negative values of net functions have been penalized to avoid
non-physical inferences of fission yields. Presently the network with double
hidden layers has optimal performances compared to the single-layer or deeper
networks. These studies are essential for further developments of precise
evaluation methods.
