mlsanityData to 7 Oct 2026

BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python

arXiv 2602.07098 ↗First posted 6 Feb 2026stat.CO cs.LG stat.MLcited by 7

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Abstract (arXiv)

Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows. An overarching motif of many Bayesian methods is that they are relatively slow, which often becomes prohibitive when fitting complex models to large data sets. Amortized Bayesian inference (ABI) offers a path to solving the computational challenges of Bayes. ABI trains neural networks on model simulations, rewarding users with rapid inference of any model-implied quantity, such as point estimates, likelihoods, or full posterior distributions. In this work, we present the Python library BayesFlow, Version 2.0, for general-purpose ABI. Along with direct posterior, likelihood, and ratio estimation, the software includes support for multiple popular deep learning backends, a rich collection of generative networks for sampling and density estimation, complete customization and high-level interfaces, as well as new capabilities for hyperparameter optimization, design optimization, and hierarchical modeling. Using a case study on dynamical system parameter estimation, combined with comparisons to similar software, we show that our streamlined, user-friendly workflow has strong potential to support broad adoption.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 66 of its 98 references that are in the map. First posted on arXiv 6 Feb 2026.

Builds on

66 of 98 references.

References · 66

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026Unsupervised Continual Learning for Amortized Bayesian Inferencecited by 0
  2. 2026Identifiability and amortized inference limitations in Kuramoto modelscited by 0
  3. 2026Inverting Foundation Models of Brain Function with Simulation-Based Inferencecited by 0
  4. 2026Conservative neural posterior estimation via distributionally robust trainingcited by 0
  5. 2026Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Modelcited by 0
  6. 2026High-Dimensional Simulation-Based Inference in Latent Spacescited by 0
  7. 2026Amortized Bayesian Inference on Multilevel Models of Arbitrary Structurecited by 0