When adjusting nuclear data with integral experiments, care must be taken that spurious adjustments are not made by assimilating poorly characterized integral parameters. If there are unaccounted for biases or poorly estimated uncertainties in the calculated and experimental values for an integral parameter, the Bayesian data assimilation may adjust the nuclear data in a manner that does not reflect the physics of the integral parameter. To identify and lessen the impact of these inconsistent integral parameters, we present a Marginal Likelihood Optimization algorithm. In a data-driven way, the marginalized likelihood is used to modulate hyperparameter terms that decrease the influence of inconsistent integral parameters on the adjustment. The advantage of this approach over other methods in the literature is that it incorporates correlation information and does not remove an integral parameter from the adjustment. Herein, we present and motivate the algorithm, and apply it to an integral data assimilation case study.

当使用积分实验调整核数据时，必须注意不要通过吸收特性不佳的积分参数来进行虚假调整。如果积分参数的计算值和实验值中没有说明偏差或估计不确定性不佳的情况，则贝叶斯数据同化可能会以不反映积分参数物理性质的方式调整核数据。为了识别并减轻这些不一致的积分参数的影响，我们提出了一种边际似然优化算法。以数据驱动的方式，边缘化的可能性用于调制超参数项，以减少不一致的积分参数对调整的影响。与文献中的其他方法相比，此方法的优势在于它合并了相关信息，并且不会从调整中删除积分参数。在这里，我们提出并激励该算法，并将其应用于完整的数据同化案例研究。