mlsanityData to 7 Oct 2026

Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap

arXiv 2202.04744 ↗First posted 9 Feb 2022stat.ME cs.LG stat.MLcited by 41

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Abstract (arXiv)

Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena, and as such can often be misspecified in practice. Unfortunately, existing Bayesian approaches for simulators are known to perform poorly in those cases. In this paper, we propose a novel algorithm based on the posterior bootstrap and maximum mean discrepancy estimators. This leads to a highly-parallelisable Bayesian inference algorithm with strong robustness properties. This is demonstrated through an in-depth theoretical study which includes generalisation bounds and proofs of frequentist consistency and robustness of our posterior. The approach is then assessed on a range of examples including a g-and-k distribution and a toggle-switch model.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 33 of its 85 references that are in the map. First posted on arXiv 9 Feb 2022.

Builds on

33 of 85 references.

References · 33

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21 more
  1. 2019MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancycited by 35
  2. 2017General Bayesian Updating and the Loss-Likelihood Bootstrapcited by 34
  3. 2019Approximate Bayesian computation with the Wasserstein distancecited by 34
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Citing papers per year: 2022: 4; 2023: 12; 2024: 7; 2025: 14; 2026: 4.

41 papers.

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  1. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  2. 2022Investigating the Impact of Model Misspecification in Neural Simulation-based Inferencecited by 44
  3. 2022Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationcited by 27
  4. 2024Addressing Misspecification in Simulation-based Inference through Data-driven Calibrationcited by 26
  5. 2023Sensitivity-Aware Amortized Bayesian Inferencecited by 20
  6. 2023Adversarial robustness of amortized Bayesian inferencecited by 17
  7. 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationcited by 17
  8. 2023Robust and Scalable Bayesian Online Changepoint Detectioncited by 16
  9. 2022Targeted Separation and Convergence with Kernel Discrepanciescited by 15
  10. 2022Generalised Bayesian Inference for Discrete Intractable Likelihoodcited by 14
  11. 2023Robust and Conjugate Gaussian Process Regressioncited by 14
  12. 2023Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inferencecited by 13
29 more
  1. 2025Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Datacited by 11
  2. 2024Sourcerer: Sample-based Maximum Entropy Source Distribution Estimationcited by 9
  3. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
  4. 2025A Dictionary of Closed-Form Kernel Mean Embeddingscited by 9
  5. 2023Misspecification-robust Sequential Neural Likelihood for Simulation-based Inferencecited by 8
  6. 2024Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigationcited by 8
  7. 2024Robust Kernel Hypothesis Testing under Data Corruptioncited by 7
  8. 2024On the Robustness of Kernel Goodness-of-Fit Testscited by 7
  9. 2024Cost-aware simulation-based inferencecited by 6
  10. 2025TabMGP: Martingale Posterior with TabPFNcited by 6
  11. 2024A Comprehensive Guide to Simulation-based Inference in Computational Biologycited by 5
  12. 2025Robust Simulation-Based Inference under Missing Data via Neural Processescited by 5
  13. 2023A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networkscited by 3
  14. 2026Minimum Distance Summaries for Robust Neural Posterior Estimationcited by 3
  15. 2023Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Casescited by 2
  16. 2025SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence eracited by 2
  17. 2025Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Setscited by 2
  18. 2023A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancycited by 1
  19. 2025Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelscited by 1
  20. 2025Causal Posterior Estimationcited by 1
  21. 2025Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inferencecited by 1
  22. 2025Multi-Output Robust and Conjugate Gaussian Processescited by 1
  23. 2025Robust Bayesian Optimisation with Unbounded Corruptionscited by 1
  24. 2023Pseudo-Likelihood Inferencecited by 0
  25. 2025A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimationcited by 0
  26. 2025Misspecification-robust amortised simulation-based inference using variational methodscited by 0
  27. 2026Unsupervised Continual Learning for Amortized Bayesian Inferencecited by 0
  28. 2026Variational predictive resamplingcited by 0
  29. 2026A Gradient Flow Perspective on Minimum MMD Estimationcited by 0