mlsanityData to 7 Oct 2026

Bayesian Workflow

arXiv 2011.01808 ↗First posted 3 Nov 2020stat.MEcited by 47

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Abstract (arXiv)

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2020.

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Citing papers per year: 2021: 4; 2022: 1; 2023: 7; 2024: 19; 2025: 9; 2026: 7.

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  1. 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 60
  2. 2023BayesFlow: Amortized Bayesian Workflows With Neural Networkscited by 32
  3. 2021Pathfinder: Parallel quasi-Newton variational inferencecited by 26
  4. 2023A Primer on Bayesian Neural Networks: Review and Debatescited by 26
  5. 2023Sensitivity-Aware Amortized Bayesian Inferencecited by 20
  6. 2021Bayesian hierarchical stacking: Some models are (somewhere) usefulcited by 14
  7. 2024Scalable Bayesian Learning with posteriorscited by 13
  8. 2024Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labelingcited by 12
  9. 2024Amortized Bayesian Multilevel Modelscited by 12
  10. 2023A Deep Learning Method for Comparing Bayesian Hierarchical Modelscited by 11
  11. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  12. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
35 more
  1. 2023Misspecification-robust Sequential Neural Likelihood for Simulation-based Inferencecited by 8
  2. 2024Bayesian Neural Networks with Domain Knowledge Priorscited by 8
  3. 2024Compositional simulation-based inference for time seriescited by 8
  4. 2025Diffusion Models in Simulation-Based Inference: A Tutorial Reviewcited by 8
  5. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  6. 2025RefineStat: Efficient Exploration for Probabilistic Program Synthesiscited by 5
  7. 2024Provably Scalable Black-Box Variational Inference with Structured Variational Familiescited by 4
  8. 2024Gradient-free variational learning with conditional mixture networkscited by 4
  9. 2024Active Inference and Human--Computer Interactioncited by 4
  10. 2021Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truthscited by 3
  11. 2024On Uncertainty Quantification for Near-Bayes Optimal Algorithmscited by 3
  12. 2024Amortized Bayesian Workflowcited by 3
  13. 2024Predictive variational inference: Learn the predictively optimal posterior distributioncited by 3
  14. 2025Amortized Bayesian Mixture Modelscited by 3
  15. 2025PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inferencecited by 3
  16. 2026Minimum Distance Summaries for Robust Neural Posterior Estimationcited by 3
  17. 2023A State-Space Perspective on Modelling and Inference for Online Skill Ratingcited by 2
  18. 2024Bayesian Inverse Graphics for Few-Shot Concept Learningcited by 2
  19. 2024Thermodynamic Bayesian Inferencecited by 2
  20. 2024Improvement and generalization of ABCD method with Bayesian inferencecited by 1
  21. 2024Expert-elicitation method for non-parametric joint priors using normalizing flowscited by 1
  22. 2025Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Networkcited by 1
  23. 2026Scalable Simulation-Based Model Inference with Test-Time Complexity Controlcited by 1
  24. 2026Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Modelscited by 1
  25. 2021Technology Readiness Levels for Machine Learning Systemscited by 0
  26. 2022Multi-Model Probabilistic Programmingcited by 0
  27. 2023rTisane: Externalizing conceptual models for data analysis increases engagement with domain knowledge and improves statistical model qualitycited by 0
  28. 2024Hierarchical Bayesian Crowdsourcing with Item Difficultycited by 0
  29. 2024Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizationscited by 0
  30. 2024Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systemscited by 0
  31. 2025Bayesian Inverse Physics for Neuro-Symbolic Robot Learningcited by 0
  32. 2025Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertaintycited by 0
  33. 2026Recovery-Directed Symbolic Distillation of Neural Likelihoodscited by 0
  34. 2026Amortized Bayesian Inference on Multilevel Models of Arbitrary Structurecited by 0
  35. 2026Large Language Bayes Is Not Reparameterisation-Invariantcited by 0