  A mathematical model is a representation of a physical system depending on
unknown parameters. Calibration refers to attributing values to these
parameters, using observations of the physical system, acknowledging that the
mathematical model is an inexact representation of the physical system. General
Bayesian inference generalizes traditional Bayesian inference by replacing the
log-likelihood in Bayes' theorem by a (negative) loss function. Methodology is
proposed for the general Bayesian calibration of mathematical models where the
resulting posterior distributions estimate the values of the parameters that
minimize the L2 norm of the difference between the mathematical model and true
physical system.
