  In robotics, simulation has the potential to reduce design time and costs,
and lead to a more robust engineered solution and a safer development process.
However, the use of simulators is predicated on the availability of good
models. This contribution is concerned with improving the quality of these
models via calibration, which is cast herein in a Bayesian framework. First, we
discuss the Bayesian machinery involved in model calibration. Then, we
demonstrate it in one example: calibration of a vehicle dynamics model that has
low degree of freedom count and can be used for state estimation, model
predictive control, or path planning. A high fidelity simulator is used to
emulate the ``experiments'' and generate the data for the calibration. The
merit of this work is not tied to a new Bayesian methodology for calibration,
but to the demonstration of how the Bayesian machinery can establish
connections among models in computational dynamics, even when the data in use
is noisy. The software used to generate the results reported herein is
available in a public repository for unfettered use and distribution.
