Technical difficulties: new stream here youtube.com/live/2lcCqjM...
03.03.2026 16:10
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Technical difficulties: new stream here youtube.com/live/2lcCqjM...
I have added a new tutorial on discrete diffusion models:
github.com/gpeyre/ot4ml
My talk on "Inference for group interaction experiments" from the Foundations of Causal Inference workshop at the Isaac Newton Institute is available via their Youtube channel: youtu.be/3hh-bM8YNSc?...
It's been hard (it's being hard?) to learn how to make human connections. It's still a learnable skill.
We just released Polars 1.37, here are the highlights:
Improved Streaming Sinks: 1.14x-1.88x speedup, ~10% of the original memory.
Streaming Compressed CSVs
Faster SQL Ordering
pl.PartitionBy
min_by / max_by (see below)
Series.sql()
Free-Threading Support
Python 3.9 Support Dropped
musl Builds
@anthropic.com is investing $1.5 million in the PSF, focused on security. These funds will make an enormous impact on the PSF and the security of millions of #Python and @pypi.org users. Please join us in thanking Anthropic for this landmark gift!
Read more on our blog:
This is a good thread. My not particularly hot take is that there is no causal inference, there is only predictive inference and CI is mostly a correction away from the bad NP-style “a p-value tells me if moving x by one unit has a significant effect” thinking. But like you don’t need DAGs for that.
A satisfying read! I must concede that I am fairly ignorant of the broader causal literature, and am a priori particularly suggestible to the claims herein.
arxiv.org/abs/2512.23408
'Probabilistic Modelling is Sufficient for Causal Inference'
- Bruno Mlodozeniec, David Krueger, Richard E. Turner
I’m excited that MIT News covered our new paper on confidence intervals for associations in spatial settings!
news.mit.edu/2025/new-met...
A new version of scikit-learn has been released 🥳 check out the highlights: scikit-learn.org/stable/auto_...
Thanks everyone who contributed to this release!
Let me know what you think of the experimental GPU support
course schedule as a table. Available at the link in the post.
I'm teaching Statistical Rethinking again starting Jan 2026. This time with live lectures, divided into Beginner and Experienced sections. Will be a lot more work for me, but I hope much better for students.
I will record lectures & all will be found at this link: github.com/rmcelreath/s...
Only a few more days to register for my charity regression course on Wednesday. All material, including slides and recordings, will be made available for those who cannot attend live. A few sponsored registrations still available. Registration details at betanalpha.github.io/courses/.
What do you consider lacking in JAX compared to PyTorch?
Brmspy: Python-first access to brms (cmdstanr backend, ArviZ output) by Braffolk discourse.mc-stan.org/t/brmspy-pyt...
pandas 3.0 rc demo
😱🙀 The pandas 3.0 release-candidate is here!
💥 Will it break your code?
💡 Test it with `uv pip install -U --pre pandas` to find out!
🌊🦄 Narwhals users can relax, everything's taken care of for you, no need to do anything ☺️
Oh boy, you can bet we are cooking the coolest profiler for Python 3.15 👨🍳🔥
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.
this is happening on Friday!
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
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
Bill Engels brought Gaussian Processes to life at PyData Seattle 2025.
From hierarchical models to a baseball performance case study, this #PyMC-powered talk shows how to model uncertainty with confidence.
Watch here: dub.link/Qm1q9ju
Trying to learn more about fixed effects. I wrote this for me, maybe this is useful for you too dpananos.github.io/posts/2025-1...
The newest chapter of Think Linear Algebra is up now!
It is about least squares regression, QR decomposition, and orthogonality:
allendowney.github.io/ThinkLinearA...
🎉 The program for this year's Causal Data Science Meeting (#CDSM2025) is now live!
📅 Nov 12–13, 2025 | 💻 Online | 🎟️ Free registration
Join us for two days of talks and debates at the intersection of causality, data science, and AI.
👉 causalscience.org
🎥 The Wednesday conference talks are now live! ✨ Watch them now on our YouTube channel: www.youtube.com/@EuroPythonC...
🚀 New talk!
"Automated ML-guided lead optimization: surpassing human-level performance at protein engineering"
▶️ www.youtube.com/watch?v=mEhB...
✨🧪 This was a talk I gave at the recent AIxBIO conference in Cambridge UK. A 10-minute pitch for what we do at Cradle!
Whoa—my book is up for pre-order!
𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData
The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit
The web version will stay free forever and my proceeds go to charity.
tinyurl.com/4fk56fc8
A nice primer on normalizing flows by PyMC/PyTensor devs Ricardo and Jesse. pytensor.readthedocs.io/en/latest/ga...
PyMC people: Is there a way to implement a weighted formulation of a discrete count likelihood like the poisson, discrete weibull, etc? In Stan I'd typically do this via something like
for(n in 1:N)
target += ({function}(args...) * weights[n]);
