  The optimisation (tuning) of the free parameters of Monte Carlo event
generators by comparing their predictions with data is important since the
simulations are used to calculate experimental efficiency and acceptance
corrections, or provide predictions for signatures of hypothetical new
processes in experiments. We present a tuning procedure that is based on
Bayesian reasoning and that allows for a proper statistical interpretation of
the results. The parameter space is fully explored using Markov Chain Monte
Carlo. We apply the tuning procedure to the Herwig7 event generator with both
the cluster and the string hadronization models and a large set of measurements
from hadronic Z-boson decays produced at LEP in $e^{+}e^{-}$ collisions.
Furthermore, we introduce a coherent propagation of uncertainties from the
realm of parameters to the realm of observables and we show the effects of
including experimental correlations of the measurements. To allow comparison
with the approaches of other groups, we repeat the tuning considering weights
for individual measurements.
