  Upcoming large-scale spectroscopic surveys with e.g. WEAVE and 4MOST will
provide thousands of spectra of massive stars, which need to be analysed in an
efficient and homogeneous way. Usually, studies of massive stars are limited to
samples of a few hundred objects which pushes current spectroscopic analysis
tools to their limits because visual inspection is necessary to verify the
spectroscopic fit. Often uncertainties are only estimated rather than derived
and prior information cannot be incorporated without a Bayesian approach. In
addition, uncertainties of stellar atmospheres and radiative transfer codes are
not considered as a result of simplified, inaccurate or incomplete/missing
physics or, in short, idealised physical models.
  Here, we address the question of "How to compare an idealised model of
complex objects to real data?" with an empirical Bayesian approach and maximum
a {\it posterior} approximations. We focus on application to large scale
optical spectroscopic studies of complex astrophysical objects like stars. More
specifically, we test and verify our methodology on samples of OB stars in 30
Doradus region of the Large Magellanic Clouds using a grid of FASTWIND model
atmospheres.
  Our spectroscopic model de-idealisation analysis pipeline takes advantage of
the statistics that large samples provide by determining the model error to
account for the idealised stellar atmosphere models, which are included into
the error budget. The pipeline performs well over a wide parameter space and
derives robust stellar parameters with representative uncertainties.
