sbi - Simulation-based inference

sbi v.0.26.1 is out 🎉. We initially planned this release for January, but then the Grenoble Hackathon and GSoC applications happened. Now we have three new methods, better neural nets, cleaner internals, better docs, and 9 new contributors 🤗. Highlights below 🧵

New how-to guides: using Pyro models with sbi, hyperparameter tuning with @optuna.bsky.social, embedding time-series data, and a guide on the different abstraction levels available in sbi.

sbi v.0.26.1 is out 🎉. We initially planned this release for January, but then the Grenoble Hackathon and GSoC applications happened. Now we have three new methods, better neural nets, cleaner internals, better docs, and 9 new contributors 🤗. Highlights below 🧵

Neural nets: we ported the Mixture Density Network in-house (no more pyknos dependency) and improved its numerical stability. We now have EDM-style noise schedules for score-based models. And the transformer embedding handles 1D time-series data with automatic input projection.

sbi v.0.26.1 is out 🎉. We initially planned this release for January, but then the Grenoble Hackathon and GSoC applications happened. Now we have three new methods, better neural nets, cleaner internals, better docs, and 9 new contributors 🤗. Highlights below 🧵

🎉 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: 🧵 👇

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.

🎉 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: 🧵 👇

@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 🚀

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 🧵

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

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 🧵

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

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 🧵

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

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!

🚀 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:🧵👇

🎉 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/...

✨ 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.

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

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 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