Model misspecification is one of the trickier challenges in SBI: what happens when your simulator doesn’t capture the observed data? I wrote an overview of the problem and methods to detect and handle it, now out in the ICLR 2026 blog post track: iclr-blogposts.github.io/2026/blog/20...

SBI Hackathon Grenoble is a wrap! 🎉 35 researchers and a great hybrid format of 1.5 days of tutorials + 1.5 days of applied hackathon. Many went from “having heard of sbi” to applying full SBI workflows to their own research projects. 🧵👇

On my way from Munich to Grenoble 🚞 to co-lead a 3-day SBI tutorial + hackathon together with @danielged.bsky.social, organised by Pedro Rodriguez and @ugrenoblealpes.bsky.social. Excited to meet researchers from across France, many bringing their own simulators 🚀

After years of working on SBI methods and the sbi toolbox, we finally wrote the practical guide we wished had existed when we started. Grateful to have collaborated with researchers across many institutions to consolidate what we've learned about making these methods work in practice!

Machine Learning in Science@mackelab.bsky.social · 9mo ago

Simulation-based inference (SBI) has transformed parameter inference across a wide range of domains. To help practitioners get started and make the most of these methods, we joined forces with researchers from many institutions and wrote a practical guide to SBI. 📄 Paper: arxiv.org/abs/2508.12939

🎉 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.

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 🧵

Fun read of their amazing contributions to the SBI hackathon! 🥐 The SBI-Pyro bridge that @sethaxen.com built has a lot of potential I believe. I'll actually be presenting this work at @euroscipy.bsky.social this Wednesday - excited to share this with a broader audience. euroscipy.org/talks/KCYYTF/

Pyro Meets SBI: Unlocking Hierarchical Bayesian Inference for Complex Simulators

The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research.

euroscipy.org

Seth Axen 🪓@sethaxen.com · last yr.

Sharing this here a bit late, but @vstaros.bsky.social and I wrote a little something about our experience contributing to the @sbi-devs.bsky.social (simulation-based inference) hackathon. @mlcolab.org @mackelab.bsky.social We were obviously very hungry while writing.

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!

Great reminder of this sunny and so productive week in March and how much I enjoy being part of this dedicated and lovely group of SBI contributors! 🤗 Big thanks to all participants & co-organizers! 🎉

sbi - Simulation-based inference@sbi-devs.bsky.social · last yr.

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. 🥁 🎉

🥳Great news, our JOSS paper "sbi reloaded" has been accepted! 🎉 This community lead by the fine folks of @sbi-devs.bsky.social is very welcoming and super fun to work with! I learn with every discussion I have. paper: joss.theoj.org/papers/10.21... review: github.com/openjournals...

[REVIEW]: sbi reloaded: a toolkit for simulation-based inference workflows · Issue #7754 · openjournals/joss-reviews

Submitting author: @janfb (Jan Boelts) Repository: https://github.com/sbi-dev/sbi Branch with paper.md (empty if default branch): joss-submission-2024 Version: v0.24.0 Editor: @boisgera Reviewers: ...

github.com

We have been thinking about this for a while and now it’s here 🎉Looking forward to all the exciting SBI applications we will be discussing, and to onboarding new contributors! 🚀

sbi - Simulation-based inference@sbi-devs.bsky.social · last yr.

🎉 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/...

It's been a blast, thanks to @sbi-devs.bsky.social ! This week's hackathon was phenomenal! 🙏 😍 The sbi hackathon welcomed about 25 people in Tübingen with contributions spanning the globe , e.g. 🇺🇸🇯🇵🇧🇪🇩🇪. Wanna see, what we did? Check out the PRs👇 github.com/sbi-dev/sbi/...

Pull requests · 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 interface, sbi has ...

github.com

On my way to the SBI hackathon in Tübingen—on a EuroCity that’s overbooked, delayed, and mysteriously missing all reservation signs. People pacing the aisles, luggage blocking exits, a baby wailing in the distance… 🚆🔥😵‍💫 If only the German railway system were as user-friendly as the SBI package! 🥲

sbi - Simulation-based inference@sbi-devs.bsky.social · last yr.

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!

For everyone working with trial-based i.i.d. data and varying experimental conditions - we have you covered now! You need to train NLE only once and then can run MCMC with multiple subjects, trials and conditions, etc. Example: sbi-dev.github.io/sbi/dev/tuto... Reach out on GitHub for questions 🙋‍♂️

sbi - Simulation-based inference@sbi-devs.bsky.social · 2y ago

🎉 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/...

So happy to be part of this project and see it growing! Many thanks to all contributors and users for making it possible 🚀

sbi - Simulation-based inference@sbi-devs.bsky.social · 2y ago

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!