Alexandre Andorra

learnbayesstats.comA Researcher Stress-Tested My Bayesian Agent Skill. Here's What BrokeA researcher stress-tested my Bayesian Agent Skill on a real hierarchical model. It caught the workflow violations but missed a subtle prediction bug, had no sparsity priors, and lacked classification...

23 Mar

2 weeks ago, I published my #BayesianWorkflowSkill, which scored 100% on evals. Then, the brilliant Max Göbel actually used it on a real model. And it broke 🤨 Lesson: evals are necessary, but not sufficient. Get your tools into real hands early! Full blog post: learnbayesstats.com/blog-posts/b...

learnbayesstats.comAmortized Inference & GPUs for Stan -- Live at StanCon 2026Paul Bürkner and Steve Bronder, live at StanCon 2026, discuss amortized inference, likelihood-free models, GPU computing in Stan, and sum-to-zero constraints.

New episode is out, live from #StanCon, with the great Steve Bronder and @paulbuerkner.com 🍾 Thanks again for the invitation Aki Vehtari, Sara Hamis and Måns Magnusson!

Pierre-Simon Laplace@learnbayesstats.bsky.social

How can we make Bayesian inference faster? 🎧 In Episode 167 @alex-andorra.bsky.social @paulbuerkner.com and Steve Bronder discuss amortized inference, neural posteriors, GPUs in Stan, and GPU-ready linear algebra. Recorded live at StanCon 2026 in Uppsala! 🔗 lnkd.in/dUTHWeq3 #BayesianStatistics

Such a pleasure to host my friends Bill Engels and Jesse Grabowski, to talk #GaussianProcesses and #AIworkflows no less! Enjoy, my dear Bayesians 🖖

Pierre-Simon Laplace@learnbayesstats.bsky.social

New episode of LBS is out! @alex-andorra.bsky.social is joined by Bill Engels & Jesse Grabowski to discuss Gaussian processes, kernels, PyTensor, and PTGP, including HSGP, computational efficiency, AI-generated statistical code & more! 🔗Watch the full episode: learnbayesstats.com/episode/gaus...

Alexandre Andorra reposted

Suyog Chandramouli

@suyoghc.bsky.social

16 Sept

We kick of the visiting speakers' research seminar today at @princeton.edu with @alex-andorra.bsky.social joining us for a talk and workshop on #BayesianWorkflow and AI-assisted statistical modeling. These sessions are open to the public on zoom. For details, see the flyer below!

learnbayesstats.comHierarchical Sequential Sampling Modeling -- Alex FenglerAlex Fengler explains HSSM, amortized inference, and likelihood approximation networks for cognitive process models like the drift diffusion model

Huge pleasure to host my friend Alexander Fengler on the show, to go through his latest work on #AmortizedInference and the power of the neural nets + Bayes combination!

Pierre-Simon Laplace@learnbayesstats.bsky.social

What if we learned the likelihood instead of the posterior? In Episode 165, Alex Fengler joins @alex-andorra.bsky.social to discuss HSSM, likelihood approximation networks, amortized inference and simulation-based inference and more ... 🔗 learnbayesstats.com/episode/hssm... #Bayesian #PyMC

learnbayesstats.comBayesian Workflow - Gelman, Vehtari & McElreathAndrew Gelman, Aki Vehtari, and Richard McElreath discuss their new book on Bayesian workflow, reverse Bayes and hierarchical pooling.

1 Sept

Today's episode is a #BayesianBlockbuster, where I talk with three of the most influential teachers of the #stats world... plus a surprise somewhere in the middle 😏 It was an absolute blast to record it -- I hope it'll be as good to listen to it for you!

Pierre-Simon Laplace@learnbayesstats.bsky.social

In this special episode of Learning Bayesian Statistics, @alex-andorra.bsky.social is joined by Andrew Gelman, Aki Vehtari, and Richard McElreath to discuss Bayesian Workflow. From simulation and hierarchical pooling to causal inference, there’s a lot to unpack 🎧 lnkd.in/geX2QkxV #Bayesian

learnbayesstats.comSampling your models faster -- Seyboldt & CarlsonAdrian Seyboldt and Eliot Carlson explain Fisher divergence preconditioning, mass matrix adaptation, normalizing flows, and benchmarking samplers in Nutpie.

24 Aug

New episode is out! A very practical one, where we dive into the new research by Adrian Seyboldt, Eliot Carlson and Bob Carpenter about how to make your MCMC samplers go faster -- lots of gems in there, make sure to tune in!

Pierre-Simon Laplace@learnbayesstats.bsky.social

How can we make Hamiltonian Monte Carlo sample faster? In this episode, @alex-andorra.bsky.social , Adrian Seyboldt, and Eliot Carlson dive into HMC preconditioning, mass matrix adaptation, and Nutpie - including a median 4× speed-up across 114 models 🎧 learnbayesstats.com/episode/fast...

5 Aug

Some really interesting insights about #Bayes & #LLMs in this new episode -- hope you'll enjoy!

Pierre-Simon Laplace@learnbayesstats.bsky.social

Bayesian methods aren't going away in the age of LLMs. Christopher Krapu joins @alex-andorra.bsky.social to discuss GPUs, Gaussian Processes, probabilistic AI and more! 🎧 learnbayesstats.com/episode/baye... #bayesian #GPU #AI #LLM #Gaussianprocess #probablisticai

27 July

Episode 161 of the show is out, with @lacerbi.bsky.social ! We dive into all things neural processes, fast #Bayesian inferences, and LLMs -- hope you enjoy 🖖

Pierre-Simon Laplace@learnbayesstats.bsky.social

Episode 161 is out 🎧 In which @lacerbi.bsky.social explains why transformers are secretly neural processes, how his Amortized Conditioning Engine unifies inference and prediction, and why "amortize everything" needed a rethink. 🔗 learnbayesstats.com/episode/161-... #bayesian #bayesianinference

youtube.comAlex Andorra — Defending Bayesian StatisticsYouTube video by Increments Podcast

27 July

Such a blast to come on the show @vadenmasrani.bsky.social @incrementspod.bsky.social ! Always fun to nerd out on #Bayesian topics to start a day :)

Increments Podcast@incrementspod.bsky.social

New ep! The haters of Bayesian epistemology @vadenmasrani.bsky.social and Benny spend some time defending Bayesian statistics alongside the Stats God @alex-andorra.bsky.social. The Reverend is finally getting his comeuppance on Increments www.youtube.com/watch?v=7BZV...

Alexandre Andorra reposted

learnbayesstats.comTrain Once, Infer a Million Times: Bayesian Inference That's (Finally) Cheap at Query TimeMCMC re-runs from scratch on every new dataset. Amortized Bayesian inference pays the cost once -- then gives you a posterior in milliseconds, forever. Stefan Radev (BayesFlow creator) and I built an ...

8 June

🎙️ New episode! @alex-andorra.bsky.social sits down with Matthijs Hollanders on Bayesian occupancy models for wildlife data - what they are, why camera traps break classical approaches, and how his occARU R package handles it with hierarchical GPs and shrinkage priors. 🔗 lnkd.in/dw3WuMBg #bayes

Alexandre Andorra reposted

BayesFlow

@bayesflow.org

12 June

Check out the new baygent skill @alex-andorra.bsky.social and I wrote to automate your amortized workflows! 🚀 learnbayesstats.com/blog-posts/a...

11 July

Getting back to the basics with this one! What even *is* #BayesianStats ?? Prepare to get your mind blown by Vaden Masrani...

Pierre-Simon Laplace@learnbayesstats.bsky.social

🎙️ Bayesian epistemology is Bayesian statistics minus the statistics. In ep 160 Vaden Masrani joins @alex-andorra.bsky.social to talk about why Bayes' theorem is great with real data, why it breaks down on one-off future events with nothing to count👇 🔗 lnkd.in/d3v42BU2 #bayesianstatistics

github.comGitHub - Learning-Bayesian-Statistics/baygent-skills: A set of skills to call your agent Bayes. Thomas Bayes.A set of skills to call your agent Bayes. Thomas Bayes. - Learning-Bayesian-Statistics/baygent-skills

7 June

Big update to my #BayesianWorkflow and #CausalInference #AgentSkills ! Now teach PyMC 6 + ArviZ 1.0 first-class -- the latest best practices, baked in -- while still running on PyMC 5 if you can't upgrade yet. Your AI agent should know the newest idioms! github.com/Learning-Bay...

learnbayesstats.comBayesian Workflows, Foundation Models & SensitivityStefan Radev explains how simulations improve Bayesian workflows, how to do cheap sensitivity and multiverse analysis, and where BayesFlow is headed next.

26 May

Second part of my conversation with Stefan Radev is out, my dear Bayesians! We dive deep into how simulations improve #Bayesian workflows, how to do cheap #sensitivity and #multiverse analysis, and where #BayesFlow is headed next. Enjoy!

Pierre-Simon Laplace@learnbayesstats.bsky.social

Episode 158 is out 🎙️ @alex-andorra.bsky.social sits down with Stefan Radev to talk amortized Bayesian inference, multiverse analysis, and what a foundation model for Bayesian inference should actually look like and more.. 🔗 lnkd.in/dau9_eA7 #BayesianStatistics #AmortizedInference #MachineLearning

11 May

Latest episode is out, my dear #Bayesians! A deep dive into #AmortizedInference, what it looks like in practice, and how to teach it to your AI agents. Tune if you wanna see how to do fast, amortized inference that scales -- live, demoed by Stefan 😉

Pierre-Simon Laplace@learnbayesstats.bsky.social

🎙️ New episode alert! In this episode @alex-andorra.bsky.social & Stefan Radev dive into amortized inference, train a neural net once on sims, deploy on real data as many times as you want. They cover sim-to-real, psych & neuro as test beds, honest failure modes and more ... lnkd.in/dCY85k4g

30 Apr

New episode is out forks! BALD, BED and other fun academic acronyms 😏

Pierre-Simon Laplace@learnbayesstats.bsky.social

Most stats thinking starts with a dataset. Bayesian experimental design asks: which data should you collect first? Ep 156 of Learning Bayesian Statistics with @alex-andorra.bsky.social and Adam Foster covers: 👉Expected information gain 👉BALD 👉Deep adaptive design and more ... 🎧 lnkd.in/ebjV9xXS

11 Apr

New episode is out! I sit down with Andreas Munk to explore one of the most important -- and most underappreciated -- tensions in modern Bayesian practice: the gap between what we can do in research and what actually gets deployed in the wild. Hope you like it!

Pierre-Simon Laplace@learnbayesstats.bsky.social

🎙️ New episode of Learning Bayesian Statistics! EP 155 with @alex-andorra.bsky.social & Andreas Munk, why Bayesian inference still hasn't broken into everyday use. The barrier isn't the math, it's the mental shift 🔗 lnkd.in/gchb6bqj #Bayesian #ProbablisticProgramming

⚽ Last week, I was thrilled and honored to present our #SoccerFactorModel to Field of Play 2026 in Manchester! 🎙️ It was an absolute blast meeting all these brilliant people, and I can't thank enough the FoP team, especially Dominic Jordan and John Carney for their trust and invitation! 🧵 Thread 👇

View full thread

- SAR (Skill Above Replacement): what a player is actually worth, stripped of team effects - PAR (Performance Above Replacement): what they've shown you so far - When SAR > PAR, the player is undervalued. That's your buy signal.

soccerfactormodel.comSoccer Factor Model

- Full uncertainty quantification via MCMC, i.e not just point estimates, but credible intervals for every player You can also check out the interactive dashboard at www.soccerfactormodel.com Feel free to reach out if you're curious, and already looking forward to Field of Play 2027 🖖

SFM is joint work with Maximilian Göbel and tackles a problem every football club faces: how do you measure a player's true skill when raw stats are tangled up with team effects? Inspired by financial asset pricing, we built a #Bayesian model that separates a player's intrinsic scoring ability...

alexandorra.github.ioUnveiling True Talent - SFMThe Soccer Factor Model: Disentangling true player skill from team context using Bayesian inference. Presented at Field of Play 2026.

...from their team context, across 2,850+ players, 42,000+ appearances, and 24 seasons of Europe's top 5 leagues + Champions League. The full presentation is available here (alexandorra.github.io/fop_2026_sli...), but here are the key ideas:

learnbayesstats.comI Built an Agent That Refuses to Say "Causes"Coding agents confidently say "X causes Y" without drawing a DAG, checking assumptions, or running refutation tests. I built an Agent Skill that won't let them. 100% eval pass rate vs 68% without -- t...

30 Mar

Wrote up the full story behind the #CausalInference agent skill. Key insight: agents *know* causal inference, but they never stop to ask "is my DAG right?" or "does this survive a placebo test?" The skill doesn't add knowledge. It adds discipline. Full breakdown: learnbayesstats.com/blog-posts/c...

github.combaygent-skills/causal-inference at main · Learning-Bayesian-Statistics/baygent-skillsA set of skills to call your agent Bayes. Thomas Bayes. - Learning-Bayesian-Statistics/baygent-skills

26 Mar

Just released a new #AgentSkill: causal inference that refuses to say "causes" until it's earned the right. DAG before data. Assumptions before estimates. Refutation before claims. 100% eval pass rate vs 68% without the skill. The gap is in the reasoning, not the code. 👉 github.com/Learning-Bay...

learnbayesstats.comBayesian Causal Inference at ScaleThomas Pinder discusses Bayesian causal inference and Gaussian processes. Explore synthetic control and diff-in-diff for industry

25 Mar

New episode is out, my dear Bayesians! All about #CausalInference, #Experimentation at scale, and #GaussianProcesses -- definitely a fun one!

Pierre-Simon Laplace@learnbayesstats.bsky.social

New Episode Alert! 🎙️ Scaling #BayesianCausalInference with Thomas Pinder, Netflix & creator of GPJax Essential listening for anyone working at the frontier of Bayes, Experimentation & Causal Inference 📈 🔗 learnbayesstats.com/episode/154-... #Bayesian #JAX #MachineLearning #CausalInference #GPJax

Alexandre Andorra reposted

learnbayesstats.comI Taught My Coding Agent to Think Like a BayesianTeach your AI coding agent to think like a Bayesian. Discover the bayesian-workflow Agent Skill that prevents subtle statistical errors in PyMC models.

17 Mar

The show now has a blog section 🍾 Check out @alex-andorra.bsky.social 's first post!

Alexandre Andorra@alex-andorra.bsky.social

I taught my coding agent to think like a Bayesian, and wrote up what I learned. LLMs know #BayesianStats but skip half the workflow. So I built an open-source Agent Skill that enforces the full workflow every time Works with Claude Code, Cursor, Gemini, etc. learnbayesstats.com/blog-posts/b...

17 Mar

learnbaystats.comlearnbaystats.com

11 Mar

New episode is out 🍾

Pierre-Simon Laplace@learnbayesstats.bsky.social

Fundraising = Belief Updating? 🧠📉 New episode is out! I'm talking with Cherian Koshy about the Neuroscience of Philanthropy. We discuss: ✅ Why generosity is hardwired ✅ Solving the Generosity Gap ✅ Cognitive friction ✅ Ethical AI Check it out: 🔗 learnbayesstats.com/episode/neur...

Alexandre Andorra reposted

daniel saunders

@danielsaunders.bsky.social

27 Feb

at my job I’m expectedutilitymaxxing. I chatted with @alex-andorra.bsky.social on how to make it scalable and fast in an industrial context on top of the probabilistic programming language you already love, PyMC.

Alexandre Andorra@alex-andorra.bsky.social

New episode is out, where we dive into how to use #ProbabilisticModel to do #DecisionMakingOptimization -- because the best model is useless if you can't make decision from it. See you in there!

3 Mar

Just published my first open-source #AgentSkill! It's called bayesian-workflow, and helps you do #BayesianAnaylsis the right way -- well, at least I hope... Check it out here: github.com/Learning-Bay...

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

arxiv.orgDiffusion Models in Simulation-Based Inference: A Tutorial ReviewDiffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. Their score-b...

27 Feb

New episode is out, where we dive into how to use #ProbabilisticModel to do #DecisionMakingOptimization -- because the best model is useless if you can't make decision from it. See you in there!

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

Alexandre Andorra reposted

BayesFlow

@bayesflow.org

2 Jan

Diffusion models & flow matching are reshaping simulation-based inference. Thus, we wrote the first tutorial review on diffusion-based SBI. For an overview or a deep dive, check it out and let us know what you think: arXiv: arxiv.org/abs/2512.20685 Web: bayesflow-org.github.io/diffusion-ex...

topmate.ioYour All-in-One Creator StorefrontMake money from your content. Sell products, host sessions, and grow your business — all from a single link.

2 Jan

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...

Alexandre Andorra reposted

Eric Ma

@ericmjl.bsky.social

1 Jan

I don't code anymore—I build. AI lets me ship projects by describing what I want, not how to do it. In 10 days, I shipped 6 projects with OpenCode + Claude Opus 4.5. The gap between idea and product is tiny now. Would you try this? Repost or comment! buff.ly/blroW24 #ai #buildfast

buff.lyYou Can Just Make Stuff with OpenCode and Claude Opus 4.5written by Eric J. Ma on 2025-12-28 | tags: ai opencode claude automation workflow llm reasoning development review tools

2 Jan

This one is a foundational one: #ExperimentalTrialDesign the ̶r̶i̶g̶h̶t̶ Bayesian way, with no other than Scott Berry, from Berry Consultants, LLC! Enjoy, and happy new year, my dear Bayesians 🖖

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

16 Dec 2025

Very practical episode this week: What to do when you can't use MCMC but still want #BayesPerks? Well, Deterministic ADVI is the new kid on the block, and is a pretty cool one ;) #WhoisyourDADVInow

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

Alexandre Andorra reposted

2 Dec 2025

🎙️ 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

14 Nov 2025

Time to take stock of what is going on in the #TechIndustry, how #GenAI is changing the jobs, and how to integrate it in your career. Here is a super dense, and hopefully actionable, episode with the great Jordan Thibodeau -- make sure to give his SVIC Podcast a listen!

Pierre-Simon Laplace@learnbayesstats.bsky.social

🎙️ 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

3 Nov 2025

This one is a very practical one, with the great Maurizio Filippone -- all about the intersection of #DeepLearning , #GaussianProcesses and why combining them is so powerful. Enjoy!

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

Alexandre Andorra reposted

17 Oct 2025

🍽️ 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

6 Oct 2025

How to run #BART and #TreeModels fast in #Python -- new episode is out, with @gstechschulte.bsky.social !

Pierre-Simon Laplace@learnbayesstats.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-...

Alexandre Andorra reposted

9 Sept 2025

🏈 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

Alexandre Andorra reposted

Ron Yurko

@stat-ron.bsky.social

9 Sept 2025

Really enjoyed talking to @alex-andorra.bsky.social on @learnbayesstats.bsky.social! I've always enjoyed listening to this podcast and encourage you to check out all of the awesome episodes Alex has recorded: learnbayesstats.com #statistics #Bayesian #datascience #sportsanalytics

Pierre-Simon Laplace@learnbayesstats.bsky.social

23 Sept 2025

Really recommend this one, about how to blend #CausalInference and #DeepLearning in practice, with ChiRho and Pyro!

Pierre-Simon Laplace@learnbayesstats.bsky.social

🧪 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

22 Aug 2025

This was a super fun one, geeking out on LLMs, deep neural nets and optimization with Maximilian Balandat, from Meta

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

8 Aug 2025

Live episode from @imperialcollegeldn.bsky.social is out! All about #BayesianDeepLearning

Pierre-Simon Laplace@learnbayesstats.bsky.social

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

Alexandre Andorra reposted

16 July 2025

🚨 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

25 July 2025

New episode is out! A really fascinating one with Robert Ness, about his new #CausalAI book, which actually kickstarted a whole new learning path for me Robert and I are actually working on an educational project you're gonna love... Follow @learnbayesstats.bsky.social for the upcoming announcement!

Pierre-Simon Laplace@learnbayesstats.bsky.social

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