  Bayesian calibration of black-box computer models offers an established
framework to obtain a posterior distribution over model parameters. Traditional
Bayesian calibration involves the emulation of the computer model and an
additive model discrepancy term using Gaussian processes; inference is then
carried out using MCMC. These choices pose computational and statistical
challenges and limitations, which we overcome by proposing the use of
approximate Deep Gaussian processes and variational inference techniques. The
result is a practical and scalable framework for calibration, which obtains
competitive performance compared to the state-of-the-art.
