Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
This work addresses the problem of computationally expensive Bayesian inference for researchers and practitioners in fields like scientific computing, offering a method to reduce simulation costs while maintaining reliability, though it is incremental as it builds on existing surrogate and amortized inference techniques.
The paper tackles the computational challenge of Bayesian inference for expensive models by proposing a framework that combines surrogate modeling with amortized Bayesian inference while quantifying surrogate uncertainties, enabling reliable and fast inference even under tight time constraints.
Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortized Bayesian Inference (ABI) can become computationally challenging. While ABI enables fast inference after training, generating sufficient training data still requires thousands of model simulations, which is infeasible for expensive models. Surrogate models offer a solution by providing approximate simulations at a lower computational cost, allowing the generation of large data sets for training. However, the introduced approximation errors and uncertainties can lead to overconfident posterior estimates. To address this, we propose Uncertainty-Aware Surrogate-based Amortized Bayesian Inference (UA-SABI) -- a framework that combines surrogate modeling and ABI while explicitly quantifying and propagating surrogate uncertainties through the inference pipeline. Our experiments show that this approach enables reliable, fast, and repeated Bayesian inference for computationally expensive models, even under tight time constraints.