Bayesian Calibration

Bayesian calibration aims to improve the accuracy and reliability of predictive models by incorporating prior knowledge and quantifying uncertainty in model parameters. Current research focuses on applying Bayesian methods to diverse model types, including neural networks, agent-based models, and physical models described by differential equations, often employing techniques like Markov Chain Monte Carlo (MCMC), variational inference, and conformal prediction to handle computational challenges and achieve better calibration. This work is significant because improved calibration leads to more trustworthy predictions across various fields, from epidemiology and healthcare to engineering and environmental science, enabling better decision-making under uncertainty.