Pierre-Simon Laplace

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 ;)

Some people think R² doesn’t belong in Bayesian models
👇 David Kohns disagrees, and he has the math to back it

🎙️Ep. 134: @alex-andorra.bsky.social sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling

🎧 learnbayesstats.com/episode/134-...

Ask me any questions you may have about this! #stats

Learning Bayesian Statistics – Laplace to be for new & veteran Bayesians alike! Laplace to be for new & veteran Bayesians alike!

🎙️ Ep. 133 is out now!

@alex-andorra.bsky.social chats with ‪ @spinkney.bsky.social
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more

🎧 learnbayesstats.com/episode/133-...

#Bayesianstats #podcast #LBS

🎙️ In episode #132 of LBS, @alex-andorra.bsky.social talks with Tom Griffiths about Bayesian cognition and human-AI interaction—how we learn from limited data, why priors matter, what AI gets wrong, and why solving real problems beats perfect models & more ...

🔗 learnbayesstats.com/episode/132-...

⚽️ New Learning Bayesian Stats ep!

@alex-andorra.bsky.social & Luke Bornn dive into how tracking data, probabilistic models & optimization are reshaping sports decisions.

🎧 Listen now: learnbayesstats.com/episode/131-...

#Datascience #Optimization #SportsAnalytics #BayesStats
#Decisionmaking

The ARR2 Prior: Flexible Predictive Prior Definition for Bayesian Auto-Regressions We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced R2. Compared to other priors designed for times-series models, the ARR2 prior allows for flexible and intuitive shrinkage. We derive the prior for pure auto-regressive models, and extend it to auto-regressive models with exogenous covariates, and state-space models. Through both simulations and real-world modelling exercises, we demonstrate the efficacy of the ARR2 prior in improving sparse and reliable inference, while showing greater inference quality and predictive performance than other shrinkage priors. An open-source implementation of the prior is provided.

David will demo #TimeSeries models -- AR, MA, ARMA, auto-regressive distributed lag (ARDL) and vector auto-regressive (VAR) models.

👉 Paper: projecteuclid.org/journals/bay...