BayesFlow
The facts below are what the maker publishes about BayesFlow, checked and dated. Our editors have not written about it yet.
The facts
Each one read from BayesFlow's own pages, with the date it was checked.
What it does
- Backend requirement
- BayesFlow requires a separately installed JAX, PyTorch, or TensorFlow backend to run.checked 11 Oct 2026
- Backends
- BayesFlow uses Keras 3 and supports JAX, PyTorch, and TensorFlow backends.checked 11 Oct 2026
- Bayesian analysis
- Yeschecked 23 Sep 2026
- Deep learning backends
- BayesFlow supports Keras 3 with JAX, PyTorch, or TensorFlow backends.checked 11 Oct 2026
- Deployment
- desktopchecked 23 Sep 2026
- Diagnostics
- The documentation demonstrates default diagnostics for inspecting inference results.checked 11 Oct 2026
- Inference workflows
- It provides workflows for posterior, likelihood, ratio, point, and model comparison estimation.checked 11 Oct 2026
- Installation
- The project documents installation from PyPI with pip or uv, and installation from source for the latest features.checked 11 Oct 2026
- Integration: Mamba
- An optional Mamba summary network is backed by the mamba-ssm library.checked 11 Oct 2026
- Integration: PyMC
- The optional PyMC wrapper lets trained ratio or continuous approximators act as likelihood terms in PyMC models.checked 11 Oct 2026
- Integrations
- Optional wrappers integrate Mamba summary networks through mamba-ssm and allow BayesFlow approximators to be used as likelihood terms in PyMC models.checked 11 Oct 2026
- Intended audience
- The package metadata lists developers and science or research as intended audiences.checked 11 Oct 2026
- License
- The package metadata identifies BayesFlow as MIT licensed.checked 11 Oct 2026
- Limit
- BayesFlow will not run without an installed machine-learning backend.checked 11 Oct 2026
- Maintainers
- The project is managed by researchers from Rensselaer Polytechnic Institute, TU Dortmund University, and Heidelberg University.checked 11 Oct 2026
- Modularity
- The simulator, adapter, summary network, inference network, and training strategy can be swapped independently.checked 11 Oct 2026
- Neural models
- Its listed generative models include diffusion and consistency models.checked 11 Oct 2026
- Product
- BayesFlow is a Python library for simulation-based amortized Bayesian inference with neural networks.checked 11 Oct 2026
- Purpose
- It trains neural networks on simulated datasets to produce Bayesian quantities such as posterior samples, likelihoods, likelihood ratios, point estimates, and model probabilities.checked 11 Oct 2026
- PyMC limitation
- The documentation says its PyMC integration is currently supported only with the JAX backend.checked 11 Oct 2026
- Python versions
- The current installation instructions state support for Python 3.12 to 3.13.checked 11 Oct 2026
- Regression models
- Nochecked 23 Sep 2026
- Scripting support
- Yeschecked 23 Sep 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
