Variational Calibration of Computer Models
This work addresses scalability issues in computer model calibration, which is incremental as it builds on existing Bayesian calibration frameworks.
The paper tackled the computational and statistical challenges in Bayesian calibration of black-box computer models by proposing a framework using approximate Deep Gaussian processes and variational inference, achieving competitive performance compared to state-of-the-art methods.
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.