  A partially non-ergodic ground-motion prediction equation is estimated for
Europe and the Middle East. Therefore, a hierarchical model is presented that
accounts for regional differences. For this purpose, the scaling of
ground-motion intensity measures is assumed to be similar, but not identical in
different regions. This is achieved by assuming a hierarchical model, where
some coefficients are treated as random variables which are sampled from an
underlying global distribution. The coefficients are estimated by Bayesian
inference. This allows one to estimate the epistemic uncertainty in the
coefficients, and consequently in model predictions, in a rigorous way. The
model is estimated based on peak ground acceleration data from nine different
European/Middle Eastern regions. There are large differences in the amount of
earthquakes and records in the different regions. However, due to the
hierarchical nature of the model, regions with only few data points borrow
strength from other regions with more data. This makes it possible to estimate
a separate set of coefficients for all regions. Different regionalized models
are compared, for which different coefficients are assumed to be regionally
dependent. Results show that regionalizing the coefficients for magnitude and
distance scaling leads to better performance of the models. The models for all
regions are physically sound, even if only very few earthquakes comprise one
region.
