juanitorduz
@juanitorduz.bsky.social
Statistical Rethinking 💯 xcelab.net/rm/
xcelab.net
November 7, 2025 at 10:25 PM
Statistical Rethinking 💯 xcelab.net/rm/
Proud of this decision and the community 🫶!
October 27, 2025 at 6:52 PM
Proud of this decision and the community 🫶!
Ok! So I read it and it’s amazing! I already migrated some custom ugly code to simply using datagrid 🙏
October 27, 2025 at 3:20 PM
Ok! So I read it and it’s amazing! I already migrated some custom ugly code to simply using datagrid 🙏
Indeed! I was there a week ago 🫠
October 24, 2025 at 11:17 AM
Indeed! I was there a week ago 🫠
Yes! You have support of hierarchical models and Gaussian process components. I will try to work out some examples and test the API :)
October 22, 2025 at 7:02 PM
Yes! You have support of hierarchical models and Gaussian process components. I will try to work out some examples and test the API :)
I am almost done with it (yes, it was hard to stop reading it!), and it’s a must read for anyone doing statical modeling 💪.
Btw: you can add in the online version a comment on the adoption of the (your) API by Bambi
bambinos.github.io/bambi/notebo...
Btw: you can add in the online version a comment on the adoption of the (your) API by Bambi
bambinos.github.io/bambi/notebo...
Examples – Bambi
bambinos.github.io
October 22, 2025 at 6:52 PM
I am almost done with it (yes, it was hard to stop reading it!), and it’s a must read for anyone doing statical modeling 💪.
Btw: you can add in the online version a comment on the adoption of the (your) API by Bambi
bambinos.github.io/bambi/notebo...
Btw: you can add in the online version a comment on the adoption of the (your) API by Bambi
bambinos.github.io/bambi/notebo...
Looking forward to reading it !
October 22, 2025 at 3:21 PM
Looking forward to reading it !
Thanks 😄 Actually, @nathanielforde.bsky.social ported this implementation into causalpy causalpy.readthedocs.io/en/stable/no... Check it out :)
Instrumental Variable Modelling (IV) with pymc models — CausalPy 0.5.0 documentation
causalpy.readthedocs.io
October 22, 2025 at 3:10 PM
Thanks 😄 Actually, @nathanielforde.bsky.social ported this implementation into causalpy causalpy.readthedocs.io/en/stable/no... Check it out :)
IMO 80% data science problem in the industry can be solved with a (good!) linear regression (I also consider GLM as just regressions with a link function)
October 11, 2025 at 5:53 PM
IMO 80% data science problem in the industry can be solved with a (good!) linear regression (I also consider GLM as just regressions with a link function)
Exited about notebooks in 2026 🚀
October 4, 2025 at 1:03 PM
Exited about notebooks in 2026 🚀
I have not tried this myself but this great blog (and the corresponding GitHub repository) might be helpful florianwilhelm.info/2020/10/baye...
Finally! Bayesian Hierarchical Modelling at Scale
For a long time, Bayesian Hierarchical Modelling has been a very powerful tool that sadly could not be applied often due to its high computations costs. With NumPyro and the latest advances in high-pe...
florianwilhelm.info
October 4, 2025 at 10:51 AM
I have not tried this myself but this great blog (and the corresponding GitHub repository) might be helpful florianwilhelm.info/2020/10/baye...
I'm learning causal inference “on the street” 😄
September 26, 2025 at 6:04 AM
I'm learning causal inference “on the street” 😄
Amazing! Thank you !!!
September 24, 2025 at 4:14 PM
Amazing! Thank you !!!
I went into the notebooks in detail and looks super interesting! I have never tried myself but now I’m very curious 🧐
September 18, 2025 at 6:19 PM
I went into the notebooks in detail and looks super interesting! I have never tried myself but now I’m very curious 🧐