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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/04/2026

This release brought together 20 contributors, 9 of them first-time. A big thank you to everyone who contributed code, reviews, issues, and feedback. 🙏 Try it: uv pip install sbi==0.26.1 Full changelog: github.com/sbi-dev/sbi/...

github.com

Release v0.26.0 · sbi-dev/sbi

✨ Highlights 🎉 New Contributors @vagechirkov made their first contribution in #1689 @satwiksps made their first contribution in #1695 @Dev-Sudarshan made their first contribution in #1697 @touronc...

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/04/2026

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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/04/2026

Under the hood, we refactored the training loop into modular components with typed config dataclasses. Net builder kwargs are now proper config objects instead of nested dicts: much easier to inspect and extend. And we made several security-related improvements (you never know 🤖).

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/04/2026

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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/04/2026

New methods: - Amortized VI posteriors: learn likelihood once, get posteriors for new obs instantly, no MCMC - Diffusion model guidance: change your prior or likelihood without retraining - NPE for IID data without embedding nets: via MCMC, VI & importance sampling.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/04/2026

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 🧵

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sbi - Simulation-based inference @sbi-devs.bsky.social · 17/10/2025

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🙏

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sbi - Simulation-based inference @sbi-devs.bsky.social · 17/10/2025

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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 17/10/2025

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!

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sbi - Simulation-based inference @sbi-devs.bsky.social · 17/10/2025

@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 🚀

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sbi - Simulation-based inference @sbi-devs.bsky.social · 17/10/2025

🎉 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: 🧵 👇

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Reposted by sbi - Simulation-based inference

Jan Boelts @janboelts.bsky.social · 18/09/2025

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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025

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

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025

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

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025

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

sbi.readthedocs.io

Welcome to sbi!

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025

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

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025

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

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sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025

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 🧵

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sbi - Simulation-based inference @sbi-devs.bsky.social · 20/05/2025

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!

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sbi - Simulation-based inference @sbi-devs.bsky.social · 12/05/2025

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. 🥁 🎉

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sbi - Simulation-based inference @sbi-devs.bsky.social · 04/04/2025

🎉 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/...

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sbi - Simulation-based inference @sbi-devs.bsky.social · 16/03/2025

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!

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sbi - Simulation-based inference @sbi-devs.bsky.social · 28/01/2025

🙏 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! 🙌

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sbi - Simulation-based inference @sbi-devs.bsky.social · 14/01/2025

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! 🚀

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sbi - Simulation-based inference @sbi-devs.bsky.social · 14/01/2025

🚀 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:🧵👇

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sbi - Simulation-based inference @sbi-devs.bsky.social · 30/12/2024

🙌 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!

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sbi - Simulation-based inference @sbi-devs.bsky.social · 30/12/2024

✨ 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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 30/12/2024

🎉 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/...

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sbi - Simulation-based inference @sbi-devs.bsky.social · 03/12/2024

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.com

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...

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sbi - Simulation-based inference @sbi-devs.bsky.social · 27/11/2024

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

github.com

GitHub - sbi-dev/sbi: Simulation-based inference toolkit

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

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sbi - Simulation-based inference @sbi-devs.bsky.social · 27/11/2024

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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 27/11/2024

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.

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sbi - Simulation-based inference @sbi-devs.bsky.social · 27/11/2024

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

arxiv.org

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...

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sbi - Simulation-based inference @sbi-devs.bsky.social · 18/11/2024

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!

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