  Inference of physical parameters from reference data is a well studied
problem with many intricacies (inconsistent sets of data due to experimental
systematic errors, approximate physical models...). The complexity is further
increased when the inferred parameters are used to make predictions (virtual
measurements) because parameters uncertainty has to be estimated in addition to
parameters best value. The literature is rich in statistical models for the
calibration/prediction problem, each having benefits and limitations.
  We review and evaluate standard and state-of-the-art statistical models in a
common bayesian framework, and test them on synthetic and real datasets of
temperature-dependent viscosity for the calibration of Lennard-Jones parameters
of a Chapman-Enskog model.
