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[2409.04332] Amortized Bayesian Workflow
Summary
The paper presents an Amortized Bayesian Workflow that combines fast amortized inference with accurate MCMC techniques, optimizing Bayesian inference for numerous datasets.
Why It Matters
This research addresses the critical balance between computational efficiency and accuracy in Bayesian inference, which is essential for data-driven decision-making across various fields. By integrating different inference methods, it offers a practical solution for handling large datasets effectively.
Key Takeaways
- Introduces a hybrid workflow combining amortized inference and MCMC.
- Demonstrates efficiency gains while maintaining high posterior quality.
- Utilizes principled diagnostics to select the best inference method.
- Applicable to both synthetic and real-world datasets.
- Addresses the trade-off between speed and accuracy in Bayesian methods.
Computer Science > Machine Learning arXiv:2409.04332 (cs) [Submitted on 6 Sep 2024 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Amortized Bayesian Workflow Authors:Chengkun Li, Aki Vehtari, Paul-Christian Bürkner, Stefan T. Radev, Luigi Acerbi, Marvin Schmitt View a PDF of the paper titled Amortized Bayesian Workflow, by Chengkun Li and 5 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2409.04332 [cs.LG] (or arXiv:2409.04332v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2409.04332 Focus to learn more arXi...