  Experimental calibration of dynamic thermal models is required for model
predictive control and characterization of building energy performance. In
these applications, the uncertainty assessment of the parameter estimates is
decisive; this is why a Bayesian calibration procedure (selection, calibration
and validation) is presented. The calibration is based on an improved
Metropolis-Hastings algorithm suitable for linear and Gaussian state-space
models. The procedure, illustrated on a real house experiment, shows that the
algorithm is more robust to initial conditions than a maximum likelihood
optimization with a quasi-Newton algorithm. Furthermore, when the data are not
informative enough, the use of prior distributions helps to regularize the
problem.
