This article provides a tutorial on Bayesian approaches for model calibration. It describes the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease.
Menzies NA, Soeteman D, Pandya A et al. Bayesian Methods for Calibrating Health Policy Models: A Tutorial. PharmacoEconomics 2017; 35 (6): 613-624. http://dx.doi.org/10.1007/s40273-017-0494-4