On the maintainer side, we gained valuable insights into where users struggle with API limitations or missing docs β fuel for future development @sbi-devs.bsky.social
Great team work with @danielged.bsky.social and collaboration
@tommoral.bsky.social, Pedro Rodriguez and many more
Now π π¨π πΏ
23.01.2026 18:51
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Projects spanned epidemiology, planetary physics, oceanography, gravitational waves, marine weather routing, particle physics, and medical imaging. π΅βπ«
Always amazing to see the range of areas where SBI can help.
23.01.2026 18:51
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SBI Hackathon Grenoble is a wrap! π
35 researchers and a great hybrid format of 1.5 days of tutorials + 1.5 days of applied hackathon. Many went from βhaving heard of sbiβ to applying full SBI workflows to their own research projects.
π§΅π
23.01.2026 18:51
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On my way from Munich to Grenoble π 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 π
20.01.2026 19:30
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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!
21.11.2025 16:04
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π 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: π§΅ π
17.10.2025 13:30
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Haha, yes! We are working on adding support for numpyro and pymc next.
18.09.2025 16:51
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Kudos to @sethaxen.com for implementing the Pyro wrapper that makes this possible (shipped in sbi v0.25)!
And thanks to @juanitorduz.bsky.social sharing the cookie factory exampleβit's a great accessible example for hierarchical inference.
Everything runs in Colab π
18.09.2025 14:38
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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 π₯
Check it out if you need hierarchical Bayesian inference but your simulator / model has no tractable likelihood.
18.09.2025 14:38
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From hackathon to release: sbi v0.25 is here! π
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 π€―
1/7 π§΅
09.09.2025 15:00
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Fun read of their amazing contributions to the SBI hackathon! π₯
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/
18.08.2025 12:15
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More great news from the SBI community! π
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!
20.05.2025 10:50
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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! π€
Big thanks to all participants & co-organizers! π
12.05.2025 14:38
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We have been thinking about this for a while and now itβs here πLooking forward to all the exciting SBI applications we will be discussing, and to onboarding new contributors! π
04.04.2025 08:31
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Pull requests Β· 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 ...
It's been a blast, thanks to @sbi-devs.bsky.social ! This week's hackathon was phenomenal! π π The sbi hackathon welcomed about 25 people in TΓΌbingen with contributions spanning the globe , e.g. πΊπΈπ―π΅π§πͺπ©πͺ. Wanna see, what we did? Check out the PRsπ
github.com/sbi-dev/sbi/...
21.03.2025 08:45
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On my way to the SBI hackathon in TΓΌbingenβon a EuroCity thatβs overbooked, delayed, and mysteriously missing all reservation signs. People pacing the aisles, luggage blocking exits, a baby wailing in the distanceβ¦ ππ₯π΅βπ«
If only the German railway system were as user-friendly as the SBI package! π₯²
16.03.2025 13:33
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thanks for the response! yes, that makes sense. I was wondering how exactly you sample from the augmented posterior as this was not clear to me from reading the paper. E.g., in the SBI case (DDM example), how do you sample p(theta | x_i, x_o) given that the conditions are not iid?
11.02.2025 15:16
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Really neat idea!
I had a quick look and did not quite understand how the βaugmentedβ posterior is obtained or how you can sample from it to calculate the conditional ranks.
Thanks π
06.02.2025 19:37
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Actually, I think this can be useful for mixed-effects models as well: you define a hierarchical prior and simulator with your fixed effects model and then train NLE on single trial data. At inference time, you then condition on x and conditions for each subject, or for multiple subjects at once.
03.01.2025 13:37
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Good question, thanks! When you train NLE on single trials, you can now run inference given a set of iid trials and additionally condition on corresp. set of varying experimental condition (without retraining). Perm. inv. nets are used in NPE settings, which usually requires retraining. Clarified?
02.01.2025 15:07
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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 πββοΈ
02.01.2025 09:18
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Would you mind adding me as well? I work on SBI and related methods. Thanks!
02.12.2024 10:33
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So happy to be part of this project and see it growing!
Many thanks to all contributors and users for making it possible π
27.11.2024 13:59
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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!
18.11.2024 07:26
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Great list! Thanks for initiating this. Can you please add me as well? I work on OSS for science, e.g., bsky.app/profile/sbi-...
Thanks!
27.11.2024 10:40
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Great, thanks for creating this! Please add me as well.
18.11.2024 09:58
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