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 🚀

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

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

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! 🚀

🙌 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.