Sensitivity-Aware Amortized Bayesian Inference

arXiv:2310.11122v4 Announce Type: replace-cross
Abstract: Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. While theoretically appealing, they are overwhelmingly inefficient for complex Bayesian models. In this work, we propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to efficiently integrate sensitivity analyses into simulation-based inference with neural networks. First, we utilize weight sharing to encode the structural similarities between alternative likelihood and prior specifications in the training process with minimal computational overhead. Second, …

abstract arxiv bayesian bayesian inference cs.lg inference influence modeling networks neural networks sensitivity simulation statistical stat.me stat.ml type work