sbi - Simulation-based inference

GSoC really is a win-win: students gain OSS experience with dedicated mentorship, while open source projects get substantial improvements. Thanks to Massimiliano and Abel, to Manuel for mentoring and @janboelts.bsky.social for mentoring and coordination, and of course GSoC and @numfocus.bsky.social🙏

Abel @abelabate.bsky.social improved our codebase with software engineering best practices: strong typing via dataclasses, clearly defined interfaces using protocols, and systematic refactoring. Multiple PRs transformed our internal architecture for better maintainability and developer experience.

This was a massive undertaking: masked transformers, score-based diffusion variants, comprehensive tests & tutorials - all integrated into sbi's existing API. Kudos to Massimiliano for navigating this complexity!

@nmaax.bsky.social implemented the SIMFORMER. Simformer combines transformers + diffusion models to learn arbitrary conditioning between parameters and data, enabling "all-in-one" SBI: posterior/likelihood estimation, predictive sampling, and even missing data imputation 🚀

🎉 sbi participated in GSoC 2025 through @numfocus.bsky.social and it was a great success: our two students contributed major new features and substantial internal improvements: 🧵 👇

Materials from my EuroSciPy talk "Pyro meets SBI" are now available: github.com/janfb/pyro-meets-sbi

I show how we can use @sbi-devs.bsky.social-trained neural likelihoods in pyro 🔥

Check it out if you need hierarchical Bayesian inference but your simulator / model has no tractable likelihood.

GitHub - sbi-dev/sbi: sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use ... sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has ...

This release brought together 14 first-time contributors with our core team.

A big shout out to the community and everyone contributing to this release 🙏 🙌

📦 (uv) pip install sbi --upgrade
💻 Join us: github.com/sbi-dev/sbi
Full Changelog: github.com/sbi-dev/sbi/...

Two more highlights: Your sbi-trained NLE can now be wrapped into a Pyro model object for flexible hierarchical inference. And based on your feedback, we added to(device) for priors and posteriors—switching between CPU and GPU is now even easier!

6/7

Here's where it gets wild: we unified flow matching (ODEs) and score-based models (SDEs). Train with one, sample with the other. E.g., train with the flexibility and stability of flow-matching, then handle iid data with score-based posterior sampling. 🤯

5/7

We completely rebuilt our documentation! Switched to Sphinx for a cleaner, more modular structure. No more wading through lengthy tutorials—now you get short, targeted how-to guides for exactly what you need, plus streamlined tutorials for getting started.

📚 sbi.readthedocs.io/en/latest/

4/7

New inference methods: MNPE now handles mixed discrete and continuous parameters for posterior estimation (like MNLE but for posteriors).
And for our nostalgic users: we finally added SNPE-B, that classic sequential variant you've been asking about since 2020.

3/7

After the creative burst of the hackathon in March, we spent months cleaning up, testing, and polishing. Re-basing ten exciting feature branches into main takes time—but the result is worth it.

2/7

From hackathon to release: sbi v0.25 is here! 🎉

What happens when dozens of SBI researchers and practitioners collaborate for a week? New inference methods, new documentation, lots of new embedding networks, a bridge to pyro and a bridge between flow matching and score-based methods 🤯

1/7 🧵

More great news from the SBI community! 🎉
Two projects have been accepted for Google Summer of Code under the NumFOCUS umbrella, bringing new methods and general improvements to sbi. Big thanks to @numfocus.bsky.social, GSoC and our future contributors!

A wide shot of approximately 30 individuals standing in a line, posing for a group photograph outdoors. The background shows a clear blue sky, trees, and a distant cityscape or hills.

Great news! Our March SBI hackathon in Tübingen was a huge success, with 40+ participants (30 onsite!). Expect significant updates soon: awesome new features & a revamped documentation you'll love! Huge thanks to our amazing SBI community! Release details coming soon. 🥁 🎉

🎉 Exciting news! We are lauching an sbi office hour!

Join the sbi developers Thursdays 09:45-10:15am CET via Zoom (link: sbi Discord's "office hours" channel).

Get guidance on contributing, explore sbi for your research, or troubleshoot issues. Come chat with us! 🤗

github.com/sbi-dev/sbi/...

Release v0.24.0 · sbi-dev/sbi ✨ Highlights feat: add CategoricalMADE by @jnsbck in #1269 (Major New Feature) tests: mini-sbibm by @manuelgloeckler in #1335 (Major New Feature) feat: Score-based iid sampling by @manuelgloeckle...

More details here:
github.com/sbi-dev/sbi/...

sbi 0.24.0 is out! 🎉 This comes with important new features:
- 🎯 Score-based i.i.d sampling
- 🔀 Simultaneous estimation of multiple discrete and continuous parameters or data.
- 📊: mini-sbibm for quick benchmarking.

Just in time for our 1-week SBI hackathon starting tomorrow---stay tuned for more!

🙏 Please help us improve the SBI toolbox! 🙏

In preparation for the upcoming SBI Hackathon, we’re running a user study to learn what you like, what we can improve, and how we can grow.

👉 Please share your thoughts here: forms.gle/foHK7myV2oaK...

Your input will make a big difference—thank you! 🙌

What to expect:
- Coding sessions to enhance the sbi toolbox
- Research talks & lightning talks
- Networking & idea exchange
🌍 In-person attendance is encouraged but a remote option is available.
It's free to attend, but seats are limited. Beginners are welcome! 🤗
Let’s push SBI forward—together! 🚀

🚀 Join the 4th SBI Hackathon! 🚀
The last SBI hackathon was a fantastic milestone in forming a collaborative open-source community around SBI. Be part of it this year as we build on that momentum!

📅 March 17–21, 2025
📍 Tübingen, Germany or remote
👉 Details: github.com/sbi-dev/sbi/...

More Info:🧵👇

🙌 Huge thanks to our contributors for this release, including 5 first-time contributors! 🌟

Special shoutout to:
emmanuel-ferdman, CompiledAtBirth, tvwenger, matthewfeickert, and manuel-morales-a 🎉

Let us know what you think of the new version!

✨ Highlights in v0.23.3:
- sbi is now available via condaforge 🛠️
- we now support MCMC sampling with multiple i.i.d. conditions 🎯 (this is for you, decision-making researchers)

💡 Plus, improved docs here and there, clarified SNPE-A behavior, and a couple of bug fixes.

🎉 Just in time for the end of the year, we’ve released a new version of sbi!

📦 v0.23.3 comes packed with exciting features, bug fixes, and docs updates to make sbi smoother and more robust. Check it out! 👇

🔗 Full changelog: github.com/sbi-dev/sbi/...

SBI Discord Server 🤖 · sbi-dev sbi · Discussion #1318 Dear all, we are launching an SBI Discord Server! 🎉 We want to use this server to further build a community around SBI, i.e., for sharing insights, questions and events around SBI in general and th...

We are launching an SBI Discord Server! 🎉

We want to use this server to further build a community around SBI, i.e., for sharing insights, questions and events around simulation-based inference in general and the sbi package in particular.

You are all invited to join! 🤗 github.com/sbi-dev/sbi/...

GitHub - sbi-dev/sbi: Simulation-based inference toolkit Simulation-based inference toolkit. Contribute to sbi-dev/sbi development by creating an account on GitHub.

As of today, 61 scientists and engineers have contributed to the sbi toolbox. We are extremely happy about this, and want to give a huge **thank you** to all contributors! Stay tuned for more info on events and updates! github.com/sbi-dev/sbi

For all steps of the inference process, the sbi toolbox supports strong defaults if needed, but also provides full flexibility if desired. For example, you can use a pre-configured training loop, or you can write it yourself.

The sbi toolbox implements a wide range of simulation-based inference methods. It implements NPE, NLE, and NRE (all amortized or sequential), modern neural networks (flows, flow-matching, diffusion models), samplers, and diagnostic tools.

sbi reloaded: a toolkit for simulation-based inference workflows Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challeng...

The sbi package is growing into a community project 🌍 To reflect this and the many algorithms, neural nets, and diagnostics that have been added since its initial release, we have written a new software paper 📝 Check it out, and reach out if you want to get involved: arxiv.org/abs/2411.17337

Hello, world! We are a community-developed toolkit that performs Bayesian inference for simulators. We support a broad range of methods (NPE, NLE, NRE, amortized and sequential), neural network architectures (flows, diffusion models), samplers, and diagnostics. Join us!