mlsanityData to 7 Oct 2026

Sensitivity-Aware Amortized Bayesian Inference

arXiv 2310.11122 ↗First posted 17 Oct 2023stat.ML cs.LG stat.MEcited by 20

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Abstract (arXiv)

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, we leverage the rapid inference of neural networks to assess sensitivity to data perturbations and preprocessing steps. In contrast to most other Bayesian approaches, both steps circumvent the costly bottleneck of refitting the model for each choice of likelihood, prior, or data set. Finally, we propose to use deep ensembles to detect sensitivity arising from unreliable approximation (e.g., due to model misspecification). We demonstrate the effectiveness of our method in applied modeling problems, ranging from disease outbreak dynamics and global warming thresholds to human decision-making. Our results support sensitivity-aware inference as a default choice for amortized Bayesian workflows, automatically providing modelers with insights into otherwise hidden dimensions.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 59 of its 117 references that are in the map. First posted on arXiv 17 Oct 2023.

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59 of 117 references.

References · 59

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 11; 2026: 6.

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  1. 2024All-in-one simulation-based inferencecited by 71
  2. 2025Simulation-Based Inference: A Practical Guidecited by 30
  3. 2024Amortized Probabilistic Conditioning for Optimization, Simulation and Inferencecited by 22
  4. 2025Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptationcited by 12
  5. 2025Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Datacited by 11
  6. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  7. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  8. 2025Improving the Accuracy of Amortized Model Comparison with Self-Consistencycited by 5
  9. 2025Efficient Autoregressive Inference for Transformer Probabilistic Modelscited by 4
  10. 2024Amortized Bayesian Workflowcited by 3
  11. 2025Amortized Bayesian Mixture Modelscited by 3
  12. 2025PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inferencecited by 3
8 more
  1. 2025Meta-Statistical Learning: Supervised Learning of Statistical Estimatorscited by 1
  2. 2025MIST: Mutual Information Estimation Via Supervised Trainingcited by 1
  3. 2025A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarioscited by 1
  4. 2026Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimationcited by 1
  5. 2026Unsupervised Continual Learning for Amortized Bayesian Inferencecited by 0
  6. 2026Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inferencecited by 0
  7. 2026Amortized Factor Inference Networks for Posterior Inferencecited by 0
  8. 2026Guidance for Prior Change via Density Ratio Estimationcited by 0