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On the recordRead 11 Oct 202628 facts
BayesFlow
On the record
- Backend requirement
- BayesFlow requires a separately installed JAX, PyTorch, or TensorFlow backend to run.bayesflow.org · read 11 Oct 2026
- Backends
- BayesFlow uses Keras 3 and supports JAX, PyTorch, and TensorFlow backends.bayesflow.org · read 11 Oct 2026
- Bayesian analysis
- Yesbayesflow.org · read 23 Sep 2026
- Deep learning backends
- BayesFlow supports Keras 3 with JAX, PyTorch, or TensorFlow backends.bayesflow.org · read 11 Oct 2026
- Deployment
- desktopbayesflow.org · read 23 Sep 2026
- Diagnostics
- Built-in diagnostics include calibration, parameter recovery, posterior contraction, and posterior predictive checks.bayesflow.org · read 11 Oct 2026
- Inference workflows
- It provides workflows for posterior, likelihood, ratio, point, and model comparison estimation.bayesflow.org · read 11 Oct 2026
- Installation
- The documentation provides pip and source installation instructions and says a machine-learning backend must also be installed.bayesflow.org · read 11 Oct 2026
- Integration: Mamba
- An optional Mamba summary network is backed by the mamba-ssm library.bayesflow.org · read 11 Oct 2026
- Integration: PyMC
- The optional PyMC wrapper lets trained ratio or continuous approximators act as likelihood terms in PyMC models.bayesflow.org · read 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.bayesflow.org · read 11 Oct 2026
- Intended audience
- The package metadata lists developers and science or research as intended audiences.pypi.org · read 11 Oct 2026
- License
- BayesFlow is released under the MIT License and its source code is hosted in a public GitHub repository.bayesflow.org · read 11 Oct 2026
- Limit
- BayesFlow will not run without an installed machine-learning backend.bayesflow.org · read 11 Oct 2026
- Maintainers
- The project is managed by researchers from Rensselaer Polytechnic Institute, TU Dortmund University, and Heidelberg University.bayesflow.org · read 11 Oct 2026
- Modularity
- The simulator, adapter, summary network, inference network, and training strategy can be swapped independently.bayesflow.org · read 11 Oct 2026
- Neural models
- Its listed generative models include diffusion and consistency models.github.com · read 11 Oct 2026
- Product
- BayesFlow is a Python library for simulation-based amortized Bayesian inference with neural networks.bayesflow.org · read 11 Oct 2026
