daniel saunders

It’s become fashionable in some circles to reject decision theory (and other basic statistical ideas) for vaguely political reasons.

There are valid critiques—but also some worth being wary of.

Some thoughts:
statmodeling.stat.columbia.edu/2025/10/17/s...

Are you a phd student? Are you in the vicinity of one and want to help them with their bad life choices? Then this is for you! #philsci

This is the classic shorter piece mechanism.ucsd.edu/bill/teachin... and he has a book length treatment of similar ideas in Representing and Intervening.

Ian Hacking and Hasok Chang are big proponents of thinking about philosophy of science in terms of actions and skills. I love this paper by Chang: d1wqtxts1xzle7.cloudfront.net/52270296/201...

Woo congrats to Nathan and your team! Excited to read this :)

Okay but what about when you are just trynna have a good time making stuff?

It’s an amazing package. The website also has a bunch of neat strategies to diagnose hmc samplers, ones I haven’t seen discussed elsewhere.

“We didn’t ever hide that that’s what it was. People were mad because we were calling them effects,” she says. “Then they say to us, but they’re just associations with 20 covariates. But the point is we said that from the beginning. They’re associations with 20 covariates.”

Friend telling me about how few statisticians are actually Bayesians. It's a real shame how far we have fallen from God's light.

Insanely bad. I manually turn off the tab function whenever I’m about to be notebooking for a while.

Mitzi Morris's new case study mc-stan.org/learn-stan/c... illustrates with hierarchical and spatial models the better efficiency of the new sum_to_zero_vector constrained parameter introduced in Stan 2.36 (2024-12). Mitzi used CmdStanPy, but the Stan code is the same with all interfaces

Great I’m gonna try to make it!

But which day tho?

It’s out 😮 I just checked last week to be teased with a mere abstract.

A brief history of tech disruptions:
2010: We are going to disrupt that horrible corporation, Blockbuster
2012: We are going to disrupt those awful cab monopolies
2014: We are going to disrupt the record labels
2022: We are going to disrupt reading and writing

Feels extremely true, watching how business leadership reacts to the long search process involved in finding a good model. The bayesian workflow literature largely assumes academic contexts where research papers are expected to be in development for a year or more.

🎄✨ 𝐌𝐞𝐫𝐫𝐲 𝐂𝐡𝐫𝐢𝐬𝐭𝐦𝐚𝐬 𝐚𝐧𝐝 𝐇𝐚𝐩𝐩𝐲 𝐇𝐨𝐥𝐢𝐝𝐚𝐲𝐬 𝐟𝐫𝐨𝐦 𝐏𝐲𝐌𝐂 𝐋𝐚𝐛𝐬!

🎁 This holiday season, we want to thank everyone in our community for your support and enthusiasm. We’re grateful to see so many of you using PyMC-Marketing and CausalPy

#MerryChristmas #HappyNewYear #PyMCMarketing #CausalPy #Gratitude

How Does OpenAI Imagine K-12 Education? Close Reading OpenAI's training module for educators If you’re taking a free online training, it's helpful to understand who wrote that lesson plan and why. ChatGPT Foundations for Educators is a cou...

This piece deftly puts words to my frustration with the way we talk about AI. It’s about education but it feels apt in business, software, etc
mail.cyberneticforests.com/how-does-ope...

Sfu! bsky.app/profile/hkan...
Also York! Canada has really strong terminal MAs.

briefly looking up empirical papers, giving up and resorting to nature documentaries is just ... extremely philosophy.

Evolution of Similarity-Biased Social Learning (by me + Alejandro Pérez Velilla). Now in press at Evolutionary Human Sciences.

Formalizes the long-standing idea that, for better or worse, it may be adaptive to ignore or down-weight information from outgroup sources. osf.io/preprints/so...

It seems Ludwig Boltzmann had a bit of a drinking problem.

I think @jbakcoleman.bsky.social is pretty interesting. Bayesian to boot!

New Deep Dive on Splines and Hierarchical Splines for modelling Insurance Loss curves with Bambi/PyMC.

The focus is on the contrast between interpolation, extrapolation and how including extra hierarchical structure aids generalisation.

nathanielf.github.io/posts/post-w...

This is an excellent (very short!) discussion of how to decide which methods to use.

(How can there be so many snappy and highly relevant pieces by Gelman et al. that I haven’t read?!)

I picked up the dialectical biologist at a used bookstore a couple weeks ago. It slaps. Includes a whole chapter about pranks they played on EO Wilson along with this strategy for riches and professional success.

You might checkout what nathanielf.github.io does. He was a modal logic guy who now does causal inference in industry. Or www.pymc-labs.com/team/benjami... , used to be a cognitive science prof. Both of them would be pretty curious about your work I reckon.

That truly sucks! I’m so sorry the job market treated you this way. Have you explored applied causal inference consulting for industry?

Extremely cool! I’m looking forward to digging into this paper more!

I spend the first 5 weeks teaching python and simulation-based inference. Lots of emphasis on playing with model assumptions. No textbook. I just have all the lectures written up as colab notebooks github.com/daniel-saund...