Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.07098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915882877321216 |
|---|---|
| author | Kühmichel, Lars Huang, Jerry M. Pratz, Valentin Arruda, Jonas Olischläger, Hans Habermann, Daniel Kucharsky, Simon Elsemüller, Lasse Mishra, Aayush Bracher, Niels Jedhoff, Svenja Schmitt, Marvin Bürkner, Paul-Christian Radev, Stefan T. |
| author_facet | Kühmichel, Lars Huang, Jerry M. Pratz, Valentin Arruda, Jonas Olischläger, Hans Habermann, Daniel Kucharsky, Simon Elsemüller, Lasse Mishra, Aayush Bracher, Niels Jedhoff, Svenja Schmitt, Marvin Bürkner, Paul-Christian Radev, Stefan T. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07098 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python Kühmichel, Lars Huang, Jerry M. Pratz, Valentin Arruda, Jonas Olischläger, Hans Habermann, Daniel Kucharsky, Simon Elsemüller, Lasse Mishra, Aayush Bracher, Niels Jedhoff, Svenja Schmitt, Marvin Bürkner, Paul-Christian Radev, Stefan T. Computation Machine Learning 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. |
| title | BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2602.07098 |