Install it: github.com/Learning-Bayesian-Statistics/baygent-skills
Part of a bigger project I call baygent-skills -- "a set of skills to call your agent Bayes. Thomas Bayes".
More skills coming soon! Issues and PRs welcome 🙏
PyMCheers 🖖
Enforces a 9-step workflow: prior elicitation, predictive checks, calibration (LOO-PIT, not vibes), diagnostics, and reporting adapted for non-technical audiences. All the stuff agents skip when left to their own devices.
Lean, opinionated, adapted from the cutting-edge science you hear on the show!
Hellooooooo my dear Bayesians! We just open-sourced an #AgentSkill that teaches coding agents to do #Bayesian stats properly.
No more skipped diagnostics, no more point estimates without uncertainty, no more "trace plots look fine".
Works with Claude Code, Cursor, Kimi, Gemini CLI, and more
Episode 152 is out 🎙️
Host @alex-andorra.bsky.social talks with Daniel Saunders about a Bayesian decision theory workflow.
Big idea: stop optimizing for model accuracy and start optimizing for decision value.
🔗 lnkd.in/gw_uGaZc
#Bayesian #DecisionTheory #DataScience #Optimization
🎙️ New episode out now - Episode 151: Diffusion Models in Python, a Live Demo
In this episode, @alex-andorra.bsky.social is joined by Jonas Arruda to explore how diffusion models can be used for simulation-based inference (SBI) in practice with a live Python demo and more ...
🎧 lnkd.in/gMyAfrW5
Bayesian neural networks need one thing to matter: good uncertainty. Scaling them has always been the hard part
In this episode, host @alex-andorra.bsky.social with Emmanuel Sommer, Jakob Robnik, & David Rügamer explain what’s changing, faster sampling, better dynamics & more ..
🎧 lnkd.in/g2W5cZQZ
Work in tech is changing fast, and not always in obvious ways.
@alex-andorra.bsky.social talks with Alana Karen about how AI, hiring, and management are reshaping careers behind the scenes, AI automating early work, hiring favoring familiarity … and more.
🎧 lnkd.in/gcRJVT-s
#FutureOfWork
My #AdvancedRegressionModeling course, written with the brilliant Ravin Kumar and @tomicapretto.bsky.social, is now available through my Topmate profile!
So do give it a try and let me know what you think in the comments 👇
See you soon in the Intuitive Bayes' Discourse 🖖
topmate.io/alex_andorra...
Clinical trials don’t fail because patients fail.
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
Fast Bayesian inference is great… until you’re babysitting convergence.
@alex-andorra.bsky.social is joined by Martin Ingram to explore DADVI a more predictable, less noisy approach to variational inference that makes trade-offs explicit instead of mysterious
🎧 lnkd.in/gAX2iaHz
#bayesianinference
🎙️ How do you tackle extreme physics experiments? Ethan Smith shares insights with @alex-andorra.bsky.social
✅ Bayesian inference for sparse, noisy data
✅ Priors guide well-established physical models
✅ Scaling Bayesian workflows across teams
🎧 lnkd.in/geA2kQm6
#Bayesian #LearningBayesianStats
🎙️ What does it take to grow in tech? Jordan Thibodeau shares lessons from years inside top tech cultures with @alex-andorra.bsky.social
✅ Bayesian thinking as a practical advantage
✅ AI amplifies skill, not replaces it
✅ Networking & sharing knowledge matter
🎧 lnkd.in/ghk6D6nH
#bayes #career
Now I'm also looking for a research software engineer to implement a pile of research results to R packages loo, posterior, bayesplot, projpred, priorsense, brms or/and Python packages ArviZ, Bambi and Kulprit. Apply by email with no specific deadline (see contact info at users.aalto.fi/~ave/)
Bayesian deep learning helps ML models understand their uncertainty
In this episode @alex-andorra.bsky.social talks with Maurizio Filippone about Gaussian Processes, scalable inference, MCMC, and Bayesian deep learning at scale
🎧 learnbayesstats.com/episode/144-...
#BayesianStats #AI #ML #Bayes
🍽️ Can better nutrition science come from better statistics?
In the latest episode, @alex-andorra.bsky.social chats with Christoph Bamberg about using a Bayesian mindset to make psychology & nutrition research more transparent and actionable
🎧 learnbayesstats.com/episode/143-...
#bayes #nutrition
How to run #BART and #TreeModels fast in #Python -- new episode is out, with @gstechschulte.bsky.social !
🤔How do you keep Bayesian rigor when the data’s too big to behave?
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
🧪 Causal inference is about understanding why things happen, not just what
@alex-andorra.bsky.social talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧 learnbayesstats.com/episode/141-...
#newepisode
🏈 NFL meets Bayesian stats!
In this episode @alex-andorra.bsky.social chats with Ron Yurko on
👉 Writing your own models
👉 Building a sports analytics portfolio
👉 Pitfalls of modelling expectations
👉 Using tracking data for player insights
👉 Causal thinking in football data
🎧 lnkd.in/gWz4v2JG
What if your optimization algorithm could explain its uncertainty as clearly as its results?” 🤔
In this episode🎙️ @alex-andorra.bsky.social dives into Bayesian optimization, BoTorch, and why uncertainty matters with Maximilian Balandat
🎧 Listen here: lnkd.in/gg6fcfFU
#bayesian #pytorch #podcast
Your deep learning model might be confidently wrong — and in medicine or epidemiology, that’s dangerous.
In this episode, @alex-andorra.bsky.social chats with Mélodie Monod, François-Xavier & Yingzhen Li about making neural nets more reliable, Bayesian LLMs & more
🎧 lnkd.in/gcaRQXcb
#bayes #llm
Models need more than pattern-matching.
They need causal understanding.
In this episode, Robert Ness joins @alex-andorra.bsky.social to explore:
⚡ Why models need real-world biases
🧠 How causal rep learning is reshaping AI
🤖 What it takes to add causality to DL
🎧 lnkd.in/gUnCkwEP
#bayes #podcast
🚨 MCMC or INLA?
🤯 MCMC = slow sampling.
⚡ INLA = fast, smart approximations. No chains, no waiting.
🎙️ On LBS, @alex-andorra.bsky.social talks with Haavard Rue & Janet Van Niekerk about how INLA works, when to use it, and why it’s a game-changer.
🎧 Listen: lnkd.in/gp8D-RuU
#Bayesian #MCMC
🚨 Tired of MCMC cooking your CPU for hours?
@alex-andorra.bsky.social chats with Haavard Rue & Janet van Niekerk about INLA, a fast, deterministic game-changer for inference at scale.
✅ Handles huge + complex models
✅ Works with non-Gaussian likelihoods
🎧 www.learnbayesstats.com/episode/136-...
🧲 Got 50 predictors, but only 5 that matter?
Try the Horseshoe Prior — a Bayesian approach to sparse regression that shrinks noise, not signal.
Built with Bambi + @pymc.io
🔗 Full demo: bambinos.github.io/bambi/notebo...
#BayesianStatistics #Regression #HorseshoePrior #MarketingAnalytics #PyMC
New episode is out! A very practical one, where we dive into *how* to make sure your models *actually* answer the questions you're asking...
🔍 Most Bayesian models aren’t properly checked
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
Your model says 97% confidence
But should you trust it?
Uncertainty in ML is still a hard problem
We’re hosting a meetup at Imperial College London on June 24 to dig into it — with our host @alex-andorra.bsky.social and other researchers working on better ways forward
🔗 lnkd.in/eainEJ9p
New episode is out! In this one we nerd out quite deep on zero-sum constraints, and how to make your model sample faster 💨
Thanks again for the guest star appearance @aseyboldt.bsky.social !! You're welcome back anytime ;)
