Variational Calibration of Computer Models

2018/10/26 by Sébastien Marmin, Marmin, Sébastien, Maurizio Filippone +1 · 1 citation
Computer Science · Engineering · #Advanced Control Systems Optimization #Advanced Multi-Objective Optimization Algorithms #Applications (stat.AP) #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1810.12177

openalex publication_date 2018/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Auto-Encoding Variational Bayes
• A Stochastic Approximation Method
• A Framework for Validation of Computer Models

Cited by

• Real-Time Model Calibration with Deep Reinforcement Learning

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