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 predictionsvirtual measurementsbecause parameter 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 the Lennard-Jones parameters of a Chapman-Enskog model. (c) 2017 American Institute of Chemical Engineers AIChE J, 63: 4642-4665, 2017