Can AI Learn What Experts Know? Automating Prior Elicitation with Generative Models
Today's clip is from episode 158 featuring Stefan Radev. In this conversation, Alex and Stefan explore a genuinely fascinating problem: how do you turn an expert's intuition into a mathematically valid prior distribution - and can AI help automate that process? Alex explains that prior elicitation is essentially a translation problem. Experts don't walk around thinking in probability distributions - their knowledge lives in intuitions, rules of thumb, and rough ranges. The challenge is converting that into something a Bayesian model can actually use. The traditional approach? Ask an expert for quantiles or a mean, then parameterize your prior with hyperparameters and simulate until the model-implied quantities match what the expert described. If your pipeline is differentiable end-to-end, you use gradient descent. If not, you fall back to something like Bayesian optimization. Either way, you're iterating toward a prior that genuinely reflects expert knowledge - not just a convenient assumption. But the really exciting part is what came next. In a follow-up paper, they pushed this further: instead of optimizing within a fixed parametric family (say, a Gaussian), they replaced the prior entirely with a normalizing flow - a flexible generative network - and ran the same procedure. No assumed distribution family. Just let the data and the expert's knowledge shape the prior from scratch. The catch? More flexibility means more non-identifiability and stability headaches. But the direction is clear: a fully automated, end-to-end pipeline for building priors from non-probabilistic expert knowledge. And in 2026, that pipeline could theoretically be driven by an agent. Get the full discussion here: https://learnbayesstats.com/episode/b... Support & Resources → Support the show on Patreon: / learnbayesstats → Bayesian Modeling Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122 Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Can Bayesian Priors Keep Models Simple? | PC Priors Explained

#158 Bayesian Workflows & Foundation Models, with Stefan Radev

Agentic AI In Chip Design

Hyperbolic JEPA Explained: The Future of Energy-Based AI Models. GeoWorld. V-JEPA, H-JEPA, EBMs.

Android 17 sucks. So I put Linux on a phone.

Why I Never Lead With Security — Head of Solutions Design, Steve Daniels

How To Think SO Clearly People Assume You're Brilliant

The physics behind diffusion models
![You’ll stop using ChatGPT after listening to this | Jonathan Pageau [ARC 2026]](https://i.ytimg.com/vi/yZUuKzDQSsI/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLAXTozuIcoGA_3ys1pkvHYXgL8C4Q)
You’ll stop using ChatGPT after listening to this | Jonathan Pageau [ARC 2026]

BITESIZE | The Why & How of Bayesian Deep Learning, with Vincent Fortuin

I Trained AI to Predict Sports

Feeling lost in your codebase? 5 tips to tackle AI-induced cognitive debt

I made an AI learn Stock Market Patterns
![Yann LeCun's $1B Bet Against LLMs [Part 1]](https://i.ytimg.com/vi/kYkIdXwW2AE/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLDbV4izF3i-wxevCVIn7FJjoy1vlA)
Yann LeCun's $1B Bet Against LLMs [Part 1]

The AI Skills Nobody is Teaching (And Everyone Needs) | AI Expert Ethan Mollick

Photographers Who Became Friends With Wildlife in the Sweetest Way! 😍🐾

Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026

Why Bayesian Statistics Is More Computational Than Ever

Yann LeCun: World Models: Enabling the next AI revolution

