Pierre-Simon Laplace

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 🖖

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...

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

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

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...

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

1/ Great chat with Alex Andorra aka @learnbayesstats.bsky.social about efficient inference, from amortized to surrogate-based approaches and a variety of related topics (prior-fitted networks, foundation models for inference and planning, etc.), many of which are neural processes in a trenchcoat.

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

Was so great chatting with you @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

🎙️ 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

So this is apparently happening

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

🎙️ 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

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

🎙️ 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 👇

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

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

New episode is out 🍾

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

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...

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...

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