  A Monte Carlo-based Bayesian inference model is applied to the prediction of
reactor operation parameters of a PWR nuclear power plant. In this
non-perturbative framework, high-dimensional covariance information describing
the uncertainty of microscopic nuclear data is combined with measured reactor
operation data in order to provide statistically sound, well founded
uncertainty estimates of integral parameters, such as the boron letdown curve
and the burnup-dependent reactor power distribution. The performance of this
methodology is assessed in a blind test approach, where we use measurements of
a given reactor cycle to improve the prediction of the subsequent cycle. As it
turns out, the resulting improvement of the prediction quality is impressive.
In particular, the prediction uncertainty of the boron letdown curve, which is
of utmost importance for the planning of the reactor cycle length, can be
reduced by one order of magnitude by including the boron concentration
measurement information of the previous cycle in the analysis. Additionally, we
present first results of non-perturbative nuclear-data updating and show that
predictions obtained with the updated libraries are consistent with those
induced by Bayesian inference applied directly to the integral observables.
