  MOCABA is a combination of Monte Carlo sampling and Bayesian updating
algorithms for the prediction of integral functions of nuclear data, such as
reactor power distributions or neutron multiplication factors. Similarly to the
established Generalized Linear Least Squares (GLLS) methodology, MOCABA offers
the capability to utilize integral experimental data to reduce the prior
uncertainty of integral observables. The MOCABA approach, however, does not
involve any series expansions and, therefore, does not suffer from the
breakdown of first-order perturbation theory for large nuclear data
uncertainties. This is related to the fact that, in contrast to the GLLS
method, the updating mechanism within MOCABA is applied directly to the
integral observables without having to "adjust" any nuclear data. A central
part of MOCABA is the nuclear data Monte Carlo program NUDUNA, which performs
random sampling of nuclear data evaluations according to their covariance
information and converts them into libraries for transport code systems like
MCNP or SCALE. What is special about MOCABA is that it can be applied to any
integral function of nuclear data, and any integral measurement can be taken
into account to improve the prediction of an integral observable of interest.
In this paper we present two example applications of the MOCABA framework: the
prediction of the neutron multiplication factor of a water-moderated PWR fuel
assembly based on 21 criticality safety benchmark experiments and the
prediction of the power distribution within a toy model reactor containing 100
fuel assemblies.
