BayesFlow - 2.0.7 - More Powerful Diagnostics, Bugfixes, and Automatic Selection of Backends

What's Changed

More expressive diagnostics for joint calibration. Better pair plots with arbitrary quantities.
Better default settings for DeepSets and Diffusion Models.
Automatic selection of backends. No more fiddling with os.environ unless needed!
Minor bugfixes
New tutorial on spatial data

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 6 months ago

BayesFlow - 2.0.6 - Stateless Adapters, Augmentations, Bugfixes, and Better Diffusion Models

Various score-based diffusion models are now directly available in the networks module.
ECDF Calibration plots support arbitrary test statistics for more expressive diagnostics
Adapters are now completely stateless, leading to less hassle for standardization
A new module augmentations will collect non-serializable transforms applied during training. NNPE for robust training (Ward et al., 2022) has been added, and many more are coming!
Small bugfixes with internal passing of arguments have been applied
Two new tutorial notebooks are available on likelihood learning and multimodal inference

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 7 months ago

BayesFlow - 2.0.5-patch - Log gamma statistic and standardization layer fix

What's Changed

An important bugfix for standardization layers on multi-input networks and 3D > inputs.
Added log gamma statistic for better simulation-based checking as proposed by: Modrák, M., Moon, A. H., Kim, S., Bürkner, P., Huurre, N., Faltejsková, K., ... & Vehtari, A. (2025). Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity. Bayesian Analysis, 20(2), 461-488.

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 8 months ago

BayesFlow - 2.0.4 -Diffusion models, bugfixes, new standardization utility, and more

🚀 BayesFlow v2.0.4 – Flexibility and Stability

We’re excited to announce BayesFlow v2.0.4 – a major release packed with stability upgrades, smarter networks, diffusion models, and tools for multimodal inference.

✨ Highlights

🔁 Diffusion Models for Inference

Integrated a flexible DiffusionModel implementation following Kingma et al. (2023)
Added SDE solvers and flexible sampling support. You can try out different diffusion model flavors!
Unified behavior across inference networks and removed deprecations of subnet_kwargs

🧠 Smarter Networks and Fusion

Introduced FusionNetwork for multimodal learning via late fusion
New Group / Ungroup transforms for flexible input structuring
Redesigned how summary/inference networks are discovered and dispatched

🧪 Simulation & Data Handling

Added subsample() and take() transforms with percent-based slicing
Included *NaN replacement transform for taking care of missing values
Enabled batch simulation utilities and new dataset augmentation strategies
Improved consistency for disk/offline datasets, including shuffle control
Enabled arbitrary data augmentations to datasets for transformations applied only during training

📏 Stability and Standardization

Created new Standardization layers that are now managed by approximators - no need for stateful adapters
Introduced moving mean/variance tracking with stable zero-variance handling
Replaced unstable PositiveDefinite link with robust CholeskyFactor estimation for MVN approximate distributions
Fixed validation loss aggregation

🧮 Model Comparison & Approximators

Better handling of heterogeneous simulator outputs in model comparison
Overhauled metrics tracking with train/val split and custom metric support
Streamlined all approximators with unified .prepare_data() logic and log_prob fixes
Serialization is now safer and more consistent across backends and training stages

🧪 Diagnostics, Docs, and Dev Tools

New tutorials: likelihood estimation, multimodal simulations, and a book on Cognitive Modeling with BayesFlow
Improved pair plots, better spacing, and legend layering
Added new notebooks and polished documentation for approximators, diagnostics, and data handling
BayesFlow now officially supports Python 3.12

🧰 Under-the-Hood Improvements

Custom Sequential module to resolve Keras build/serialization issues
More robust test suite with extended coverage for transforms, metrics, networks, and approximators
Smarter dispatching of networks and dynamic simulator configurations

🔧 Breaking & Deprecated

standardize adapter transforms should be used only with precomputed loc and scale. → rely on the new built-in standardization utility of approximators!
Deprecated approx.summaries → use approx.summarize instead
Moved toward FutureWarning for deprecated features
Use probs instead of logits for ModelComparisonApproximator

👥 Special Thanks to Contributors

Big kudos to @LarsKue, @valentinpratz, @han-ol, @arrjon, @elseml, @jerrymhuang, @daniel-habermann for pushing this release forward!

👉 Ready to try it?

Install or upgrade via:

bash pip install "bayesflow>=2.0.4"

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 8 months ago

BayesFlow - v2.0.3 - fixes to serialization and new features

🚀 BayesFlow v2.0.3 Release Notes

⚠️ Important Notice: Breaking Changes

This release introduces significant changes to the serialization pipeline and is not backward-compatible with v2.0.2 or earlier. Models saved with older versions of BayesFlow will likely be incompatible with this release, but models trained in the future will be backward compatible with this release.

We strongly recommend updating your workflows and referring to the updated documentation to ensure a smooth transition.

📚 Documentation Updates

Enhanced the README.md with: - A detailed guide for migrating from BayesFlow v1.x to v2. - Clear warnings about breaking changes and missing features (e.g., hierarchical models, MCMC). - Pointers for users seeking parity with legacy functionality. See changes →

🔧 Backend Enhancements

Simplified version management: __version__ is now dynamically set using importlib.metadata.
Removed redundant exception handling for unsupported package imports.

🌀 Transform Enhancements

New Feature: Added support for log_det_jac (log-determinant Jacobian) in multiple transform classes: - Constrain - Concatenate - Drop

The Adapter class now optionally returns the log_det_jac alongside transformed data in both forward and inverse methods. These improvements enhance support for probabilistic modeling workflows involving change-of-variable techniques.

🧬 Serialization Improvements

Applied the @serializable decorator with explicit namespace="bayesflow.adapters" to key transform classes: AsSet, Broadcast, Constrain, and others. This improves the consistency and reliability of object serialization across the library, especially for save/load operations in custom pipelines.

✅ Summary

This release improves usability, extensibility, and transform robustness, especially for users of advanced transformations. While it introduces breaking changes, it also lays a more stable foundation for future developments. Stay tuned!

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 10 months ago

BayesFlow - v2.0.2 Fixes to usability, tests, and flow matching

More tests, including notebooks
Fixed optimal transport for JAX
Improved dispatch for inference and summary networks (used in workflows)
Mixture distributions as latent spaces
Improved README
Fixed a warning about missing compile_from_config when loading approximators
Add NumFOCUS affiliation
Add issue templates
Removes the need to explicitly drop unused variables in custom Adapters

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 10 months ago

BayesFlow - v2.0.1 - fixes and performance improvement

Fixes bug with optimal transport for flow matching
Improved speed of import by 1200%
Improved tests for consistency models

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 10 months ago

BayesFlow - 🚀 BayesFlow v2.0 (Neo) Has Officially Arrived!

🎉 BayesFlow 2.0 is here! 🎉

We're thrilled to officially release BayesFlow 2.0 - a major leap forward in amortized Bayesian inference using modern neural networks. Whether you're a researcher, practitioner, or just getting started with Bayesian modeling, BayesFlow 2 is built to help you go from idea to inference faster than ever.

🔥 What's New in v2.0?

Multi-backend support with Keras 3: Choose your favorite ML framework - JAX, PyTorch, or TensorFlow - and switch seamlessly.
Cleaner, faster API: New workflows and interfaces make it easier than ever to build, train, and evaluate your models.
More neural network architectures: A rich set of ready-to-use building blocks tailored for simulation-based inference.
Smarter, more flexible design: Refined from the ground up to align with the latest advances in generative AI and Bayesian modeling.

🧠 What is BayesFlow?

BayesFlow lets you harness generative neural networks for fast and flexible Bayesian inference with any simulator. Whether you're estimating parameters, comparing models, or designing experiments, BayesFlow helps you turn simulations into statistical insight.

Check out the 3-step conceptual overview:

Choose your backend – thanks to Keras 3, you're free to use JAX, PyTorch, or TensorFlow.
Define your simulator – write your model in pure Python and generate data effortlessly.
Select your inference algorithm – train flexible neural networks to estimate what matters most.

🚀 Getting Started Is Easy

```py3 import bayesflow as bf

workflow = bf.BasicWorkflow( inferencenetwork=bf.networks.CouplingFlow(), summarynetwork=bf.networks.TimeSeriesNetwork(), inferencevariables=["parameters"], summaryvariables=["observables"], simulator=bf.simulators.SIR() )

history = workflow.fitonline(epochs=15, batchsize=32, numbatchesperepoch=200) diagnostics = workflow.plotdefaultdiagnostics(testdata=300) ```

🧪 Check out our growing library of tutorials and notebooks, from basic regression to Bayesian experimental design. Even better - contribute your own!

📦 Installation

You can install the latest version directly from PyPI or GitHub.

And don’t forget to install a supported backend - recommend JAX for top performance 🚀

BayesFlow 2.0 is fast, flexible, and freaking awesome. We can’t wait to see what you build with it.

Let us know what you think, and if you create something cool - open a PR or share it with the community! 💙

The BayesFlow Team

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 10 months ago

BayesFlow - v1.1.6

What's Changed

Hotfix pip installation error with Apple Silicon by @marvinschmitt in https://github.com/stefanradev93/BayesFlow/pull/155

Full Changelog: https://github.com/stefanradev93/BayesFlow/compare/v1.1.5...v1.1.6

Scientific Software - Peer-reviewed - Python
Published by marvinschmitt almost 2 years ago

BayesFlow - v1.1.5

What's Changed

set label names in plot_recovery() by @levolz in https://github.com/stefanradev93/BayesFlow/pull/102
Development by @stefanradev93 in https://github.com/stefanradev93/BayesFlow/pull/103
add labelfontsize and valuefontsize by @LuSchumacher in https://github.com/stefanradev93/BayesFlow/pull/106
Optimization: add setting for tqdm mininterval by @vpratz in https://github.com/stefanradev93/BayesFlow/pull/108
add nrow and ncol argument where applicable by @LuSchumacher in https://github.com/stefanradev93/BayesFlow/pull/109
fix some typos and out-of-sync docstrings by @daniel-habermann in https://github.com/stefanradev93/BayesFlow/pull/111
add **kwargs to diagnostics.plot_recovery by @marvinschmitt in https://github.com/stefanradev93/BayesFlow/pull/112
Merge with dev by @stefanradev93 in https://github.com/stefanradev93/BayesFlow/pull/115
Minor improvements by @elseml in https://github.com/stefanradev93/BayesFlow/pull/117
Update README.md with forum by @elseml in https://github.com/stefanradev93/BayesFlow/pull/116
update tutorial notebook 1 by @rusty-electron in https://github.com/stefanradev93/BayesFlow/pull/120
Minimal Fix for Broken Tests by @LarsKue in https://github.com/stefanradev93/BayesFlow/pull/130
Drop Support for Python 3.9, Add Support for Python 3.11 by @LarsKue in https://github.com/stefanradev93/BayesFlow/pull/132
fix tutorial notebook 1 toc links and update some text by @rusty-electron in https://github.com/stefanradev93/BayesFlow/pull/136
Make diagnostic plots work with one-parameter models by @Kucharssim in https://github.com/stefanradev93/BayesFlow/pull/137
Fix offline training for model comparison ignoring shared context by @elseml in https://github.com/stefanradev93/BayesFlow/pull/139
README - update minimal example by @vpratz in https://github.com/stefanradev93/BayesFlow/pull/141
update TOCs of example notebooks by @rusty-electron in https://github.com/stefanradev93/BayesFlow/pull/142
Bump up to Series 1.1.5 update by @stefanradev93 in https://github.com/stefanradev93/BayesFlow/pull/143
add notebook: amortized point estimation by @vpratz in https://github.com/stefanradev93/BayesFlow/pull/145
Bayes Estimators: Loss functions with flexible signature by @han-ol in https://github.com/stefanradev93/BayesFlow/pull/147
Development by @stefanradev93 in https://github.com/stefanradev93/BayesFlow/pull/150

New Contributors

@daniel-habermann made their first contribution in https://github.com/stefanradev93/BayesFlow/pull/111
@rusty-electron made their first contribution in https://github.com/stefanradev93/BayesFlow/pull/120
@LarsKue made their first contribution in https://github.com/stefanradev93/BayesFlow/pull/130
@Kucharssim made their first contribution in https://github.com/stefanradev93/BayesFlow/pull/137
@han-ol made their first contribution in https://github.com/stefanradev93/BayesFlow/pull/147

Full Changelog: https://github.com/stefanradev93/BayesFlow/compare/v1.1.4...v1.1.5

Scientific Software - Peer-reviewed - Python
Published by stefanradev93 almost 2 years ago