juanitorduz

I love having physical books and I pay for them (I have yours 😄). But I think I’m a minority these days.

Yes!!! 💯

@rmcelreath.bsky.social this lecture was (particularly) amazing! I could not resist to replicate this in PyMC juanitorduz.github.io/fixed_random/

Nice!

I am super excited to join @pymc-labs.bsky.social full-time! I will be working on open-source projects, helping companies leverage Bayesian methods for decision-making, developing tailored educational workshops for industry practitioners, and serving as a product manager for our AI products.

Very keen to read it 🙃

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Here are two examples on causal inference and through the lens of probabilistic programming languages (PPLs):

- Introduction to Causal Inference with PPLs juanitorduz.github.io/intro_causal...

- Causal Inference with Multilevel Models: juanitorduz.github.io/ci_multilevel/

Implementations in PyMC.

Scaling Probabilistic Models with Variational Inference YouTube video by PyData

Here is the recording of my talk

PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro

Notebook: juanitorduz.github.io/intro_svi/

youtu.be/wG0no-mUMf0?...

#pydata #berlin #bayes

Causal discovery is an interesting field. But as always these algorithms work under assumptions and conditions … which as always … people just ignore 🫠

Statistical Rethinking 💯 xcelab.net/rm/

Proud of this decision and the community 🫶!

Open Science and Open Source only with Diversity, Equity, Inclusion, and Accessibility Including all of humanity is and always will be at the heart of open science.

Open Science and Open Source only with Diversity, Equity, Inclusion and Accessibility.

Inclusion is essential to science, and science is only worthwhile if it lifts everyone up together.

ropensci.org/blog/2025/02... #OpenSource #OpenScience

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 🫠

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...

Looking forward to reading it !

Thanks 😄 Actually, @nathanielforde.bsky.social ported this implementation into causalpy causalpy.readthedocs.io/en/stable/no... Check it out :)

I got mail! I can’t not wait @vincentab.bsky.social I’ll try to do many of these examples by “hand” (learning by doing).

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7 reasons to use Bayesian inference!
statmodeling.stat.columbia.edu/2025/10/11/7...

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 🚀

I have not tried this myself but this great blog (and the corresponding GitHub repository) might be helpful florianwilhelm.info/2020/10/baye...

Thank you @patrickdoupe.bsky.social

It was fun (painful 😅) to implement VAR(p) models from scratch juanitorduz.github.io/var_numpyro/

Festival der Riesendrachen
#Berlin

I'm learning causal inference “on the street” 😄