Bayesian inference is a powerful framework for making probabilistic inferences and decisions under uncertainty. Fundamental choices in modern Bayesian workflows concern the specification of the likelihood function and prior distributions, the posterior approximator, and the data. Each choice can significantly influence model-based inference and subsequent decisions, thereby necessitating sensitivity analysis. In this work, we propose a multifaceted approach to integrate sensitivity analyses into amortized Bayesian inference (ABI, i.e., 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, we leverage the rapid inference of neural networks to assess sensitivity to various data perturbations or pre-processing procedures. In contrast to most other Bayesian approaches, both steps circumvent the costly bottleneck of refitting the model(s) for each choice of likelihood, prior, or dataset. Finally, we propose to use neural network ensembles to evaluate variation in results induced by unreliable approximation on unseen data. We demonstrate the effectiveness of our method in applied modeling problems, ranging from the estimation of disease outbreak dynamics and global warming thresholds to the comparison of human decision-making models. Our experiments showcase how our approach enables practitioners to effectively unveil hidden relationships between modeling choices and inferential conclusions.

翻译：贝叶斯推断是在不确定性条件下进行概率推断和决策的强大框架。现代贝叶斯工作流中的基本选择涉及似然函数和先验分布的设定、后验近似器以及数据。每个选择都可能显著影响基于模型的推断及后续决策，因此需要进行敏感性分析。本研究提出了一种多层面方法，将敏感性分析整合到摊销贝叶斯推断（ABI，即基于神经网络的仿真推断）中。首先，我们利用权重共享在训练过程中编码替代似然和先验设定之间的结构相似性，最小化计算开销。其次，我们利用神经网络的快速推断能力评估对各种数据扰动或预处理过程的敏感性。与大多数其他贝叶斯方法相比，这两步都绕过了为每个似然、先验或数据集重新拟合模型的昂贵瓶颈。最后，我们提议使用神经网络集成来评估在未见数据上不可靠近似所导致的结果变异性。我们在应用建模问题中展示了方法的有效性，范围涉及疾病爆发动态估计、全球变暖阈值评估以及人类决策模型比较。实验表明，我们的方法如何使从业者能够有效揭示建模选择与推断结论之间的隐藏关系。