Sensitivity-Aware Amortized Bayesian Inference

Sensitivity analyses can reveal how different modeling choices affect statistical results, but they're often too slow for complex Bayesian models. We introduce sensitivity-aware amortized Bayesian inference (SA-ABI) as an efficient method that (1) uses weight sharing to capture similarities between model versions with little extra cost, (2) exploits the speed of neural networks to test sensitivity to data and preprocessing changes, and (3) applies deep ensembles to spot sensitivity caused by poor fit. Our experiments show that SA-ABI can provide automatic insights into hidden uncertainties.