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🎙️ How do you tackle extreme physics experiments? Ethan Smith shares insights with Alex Andorra

✅ Bayesian inference for sparse, noisy data
✅ Priors guide well-established physical models
✅ Scaling Bayesian workflows across teams

🎧 https://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

✅ Bayesian thinking as a practical advantage

✅ AI amplifies skill, not replaces it

✅ Networking & sharing knowledge matter

🎧 https://lnkd.in/ghk6D6nH

#bayes #career

Bayesian deep learning helps ML models understand their uncertainty

In this episode Alex Andorra talks with Maurizio Filippone about Gaussian Processes, scalable inference, MCMC, and Bayesian deep learning at scale

🎧 https://learnbayesstats.com/episode/144-why-is-bayesian-deep-learning-so-powerful-maurizio-filippone

#BayesianStats #AI #ML #Bayes

🍽️ Can better nutrition science come from better statistics?

In the latest episode, Alex Andorra chats with Christoph Bamberg about using a Bayesian mindset to make psychology & nutrition research more transparent and actionable

🎧 https://learnbayesstats.com/episode/143-transforming-nutrition-science-bayesian-methods-christoph-bamberg

#bayes #nutrition

🤔How do you keep Bayesian rigor when the data’s too big to behave?

Gabriel Stechschulte joins Alex Andorra on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.

🎧 Listen here: https://learnbayesstats.com/episode/142-bayesian-trees-deep-learning-optimization-big-data-gabriel-stechschulte

🧪 Causal inference is about understanding why things happen, not just what

Alex Andorra talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning

🎧https://learnbayesstats.com/episode/141-ai-assisted-causal-inference-sam-witty

#CausalInference #BayesianStatistics #Podcast #DataScience #AIResearch #LearningBayesianStatistics #NewEpisode

🏈 NFL meets Bayesian stats!

In this episode Alex Andorra 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

🎧 https://lnkd.in/gWz4v2JG

#bayesian #podcast #learningbayesianstatistics #SportsAnalytics #NFL #statistics

What if your optimization algorithm could explain its uncertainty as clearly as its results?” 🤔

In this episode🎙️ Alex Andorra dives into Bayesian optimization, BoTorch, and why uncertainty matters with Maximilian Balandat

🎧 Listen here: https://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 chats with Mélodie Monod, François-Xavier & Yingzhen Li about making neural nets more reliable, Bayesian LLMs & more...

🎧 https://lnkd.in/gcaRQXcb

#bayes #llm

Models need more than pattern-matching.
They need causal understanding.

In this episode, Robert Ness joins Alex Andorra to explore:

⚡ Why models need real-world biases
🧠 How causal rep learning is reshaping AI
🤖 What it takes to add causality to DL

🎧https://www.learnbayesstats.com/episode/137-causal-ai-generative-models-robert-ness

#bayes #podcast

🚨 Tired of MCMC cooking your CPU for hours?

Alex Andorra 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 and more ...

🎧 https://www.learnbayesstats.com/episode/136-bayesian-inference-at-scale-unveiling-inla-haavard-rue-janet-van-niekerk

🔍 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 conversation, Teemu Säilynoja joins Alex Andorra to explore SBC, prior predictive checks, posterior SBC and more!

This is the kind of model checking that actually changes your modeling practice.

🎧 Listen in: https://learnbayesstats.com/episode/135-bayesian-calibration-and-model-checking-teemu-sailynoja

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 and other researchers working on better ways forward

🔗 https://lnkd.in/eainEJ9p

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

🎙️In this episode Alex Andorra sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling

🎧 Listen now: https://learnbayesstats.com/episode/134-bayesian-econometrics-state-space-models-dynamic-regression-david-kohns

🎙️ Ep. 133 is out now!

Alex Andorra chats with ‪ Sean P
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more

🎧 https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt

#Bayesianstats #podcast #LBS

🎙️ In episode #132 of Learning Bayesian Statistics, Alex Andorra speaks with Tom Griffiths about 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗵𝘂𝗺𝗮𝗻-𝗔𝗜 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻.

🔑 𝗞𝗲𝘆 𝗽𝗼𝗶𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻:

🧠 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 uses math to model intelligence

📊 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝘀 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 to how humans learn and reason

🧩 𝗜𝗻𝗱𝘂𝗰𝘁𝗶𝘃𝗲 𝗯𝗶𝗮𝘀𝗲𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 how we generalize from limited data

🎯 𝗣𝗿𝗶𝗼𝗿 𝗲𝗹𝗶𝗰𝗶𝘁𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝘃𝗲𝗮𝗹𝘀 implicit beliefs behind decisions

and more ...

🎧https://learnbayesstats.com/episode/132-bayesian-cognition-and-the-future-of-human-ai-interaction-tom-griffiths