mlsanityData to 7 Oct 2026

Amortized Bayesian Workflow

arXiv 2409.04332 ↗First posted 6 Sep 2024cs.LG stat.MLcited by 3

Not on the map. Why?

Abstract (arXiv)

Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve a favorable combination of both speed and accuracy when performing inference on many observed datasets. Our approach uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling via generative neural networks to slower but guaranteed-accurate MCMC when needed. By reusing computations across steps, our workflow synergizes amortized and MCMC-based inference. We demonstrate the effectiveness of this integrated approach on several synthetic and real-world problems with tens of thousands of datasets, showing efficiency gains while maintaining high posterior quality.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 57 of its 111 references that are in the map. First posted on arXiv 6 Sep 2024.

Builds on

57 of 111 references.

References · 57

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2022Flow Matching for Generative Modelingcited by 5,426
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 2023Consistency Modelscited by 1,778
  5. 2017Deep Setscited by 1,394
  6. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  7. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  8. 2019Neural Spline Flowscited by 590
  9. 2019The frontier of simulation-based inferencecited by 490
  10. 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
  11. 2021Transformers Can Do Bayesian Inferencecited by 338
  12. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 216
38 more
  1. 2019Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyrocited by 187
  2. 2021Benchmarking Simulation-Based Inferencecited by 160
  3. 2020BayesFlow: Learning complex stochastic models with invertible neural networkscited by 139
  4. 2022Flow Annealed Importance Sampling Bootstrapcited by 124
  5. 2018Validating Bayesian Inference Algorithms with Simulation-Based Calibrationcited by 118
  6. 2023Flow Matching for Scalable Simulation-Based Inferencecited by 97
  7. 2019Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMCcited by 90
  8. 2021Annealed Flow Transport Monte Carlocited by 90
  9. 2019NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transportcited by 73
  10. 2024All-in-one simulation-based inferencecited by 71
  11. 2021Simulation Intelligence: Towards a New Generation of Scientific Methodscited by 68
  12. 2018Yes, but Did It Work?: Evaluating Variational Inferencecited by 52
  13. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  14. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 48
  15. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  16. 2020Bayesian Workflowcited by 47
  17. 2022Compositional Score Modeling for Simulation-based Inferencecited by 46
  18. 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 45
  19. 2022Variational methods for simulation-based inferencecited by 43
  20. 2015Pareto Smoothed Importance Samplingcited by 42
  21. 2024Neural Methods for Amortized Inferencecited by 36
  22. 2021Challenges and Opportunities in High-dimensional Variational Inferencecited by 35
  23. 2023BayesFlow: Amortized Bayesian Workflows With Neural Networkscited by 32
  24. 2022Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inferencecited by 28
  25. 2022Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationcited by 27
  26. 2021Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparisoncited by 26
  27. 2021Pathfinder: Parallel quasi-Newton variational inferencecited by 26
  28. 2022Reliable amortized variational inference with physics-based latent distribution correctioncited by 25
  29. 2020Variational Bayesian Monte Carlo with Noisy Likelihoodscited by 23
  30. 2023Consistency Models for Scalable and Fast Simulation-Based Inferencecited by 22
  31. 2023On Sampling with Approximate Transport Mapscited by 20
  32. 2023Sensitivity-Aware Amortized Bayesian Inferencecited by 20
  33. 2022Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivitycited by 19
  34. 2020OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germanycited by 17
  35. 2023L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inferencecited by 17
  36. 2024Diffusion posterior sampling for simulation-based inference in tall data settingscited by 17
  37. 2022Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimizationcited by 14
  38. 2023Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inferencecited by 14

50 most cited of 57 All 57 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Datacited by 11
  2. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  3. 2025Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processescited by 1