mlsanityData to 7 Oct 2026

BayesFlow: Amortized Bayesian Workflows With Neural Networks

arXiv 2306.16015 ↗First posted 28 Jun 2023cs.LG cs.AI stat.MLcited by 32

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Abstract (arXiv)

Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis. Typical problems in Bayesian workflows are the approximation of intractable posterior distributions for diverse model types and the comparison of competing models of the same process in terms of their complexity and predictive performance. This manuscript introduces the Python library BayesFlow for simulation-based training of established neural network architectures for amortized data compression and inference. Amortized Bayesian inference, as implemented in BayesFlow, enables users to train custom neural networks on model simulations and re-use these networks for any subsequent application of the models. Since the trained networks can perform inference almost instantaneously, the upfront neural network training is quickly amortized.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 28 Jun 2023.

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23 of 39 references.

References · 23

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  5. 2020BayesFlow: Learning complex stochastic models with invertible neural networkscited by 139
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  8. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 48
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11 more
  1. 2022Reliable amortized variational inference with physics-based latent distribution correctioncited by 25
  2. 2020Amortized Bayesian model comparison with evidential deep learningcited by 22
  3. 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
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 12; 2026: 9.

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  1. 2024Neural Methods for Amortized Inferencecited by 36
  2. 2025Simulation-Based Inference: A Practical Guidecited by 30
  3. 2024sbi reloaded: a toolkit for simulation-based inference workflowscited by 23
  4. 2023Consistency Models for Scalable and Fast Simulation-Based Inferencecited by 22
  5. 2023Sensitivity-Aware Amortized Bayesian Inferencecited by 20
  6. 2024Amortized Bayesian Multilevel Modelscited by 12
  7. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  8. 2024Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigationcited by 8
  9. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  10. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  11. 2025Multilevel neural simulation-based inferencecited by 5
  12. 2025Improving the Accuracy of Amortized Model Comparison with Self-Consistencycited by 5
20 more
  1. 2024Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappingscited by 3
  2. 2024Demonstrating the power and flexibility of variational assumptions for amortized neural posterior estimation in environmental applicationscited by 3
  3. 2024Amortized Bayesian Workflowcited by 3
  4. 2025Posterior SBC: Simulation-Based Calibration Checking Conditional on Datacited by 3
  5. 2024Variational Autoencoders for Efficient Simulation-Based Inferencecited by 2
  6. 2025MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Designcited by 2
  7. 2025ConDiSim: Conditional Diffusion Models for Simulation Based Inferencecited by 2
  8. 2026From Mice to Trains: Amortized Bayesian Inference on Graph Datacited by 2
  9. 2024Expert-elicitation method for non-parametric joint priors using normalizing flowscited by 1
  10. 2025Meta-Statistical Learning: Supervised Learning of Statistical Estimatorscited by 1
  11. 2025Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelscited by 1
  12. 2025metabeta -- A fast neural model for Bayesian mixed-effects regressioncited by 1
  13. 2025MIST: Mutual Information Estimation Via Supervised Trainingcited by 1
  14. 2026An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learningcited by 1
  15. 2026Preconditioned Robust Neural Posterior Estimation for Misspecified Simulatorscited by 0
  16. 2026Unsupervised Continual Learning for Amortized Bayesian Inferencecited by 0
  17. 2026ForwardFlow: Simulation only statistical inference using deep learningcited by 0
  18. 2026Recovery-Directed Symbolic Distillation of Neural Likelihoodscited by 0
  19. 2026Amortized Bayesian Inference on Multilevel Models of Arbitrary Structurecited by 0
  20. 2026Posterior sampling by source-space MCMC via prior-based few-step transport mapscited by 0