Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: How does putting a Gaussian process on unknown coordinates fix noisy location data in mineral prospecting?A: In mining and geostatistics, the classic Gaussian process model, known there as kriging, assumes you know exactly where each sampl...
Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work
Takeaways:
Q: How does putting a Gaussian process on unknown coordinates fix noisy location data in mineral prospecting?
A: In mining and geostatistics, the classic Gaussian process model, known there as kriging, assumes you know exactly where each sample was taken. Chris’ project broke that assumption on purpose: the recorded coordinates for each core sample were only accurate to within a rough radius. By treating the true locations as latent variables and putting a Gaussian process over them jointly with the measurements, the model could still reconstruct the underlying gold-concentration field, even though the exact sampling locations were never known precisely. It's a demonstration that Gaussian processes can absorb structural uncertainty that looks, at first glance, like it should make the problem impossible.
Q: What is "Poverty Bayes," and what did it cost to train a two-million-parameter Bayesian model?
A: Poverty Bayes was Chris’ experiment in seeing how cheaply a large Bayesian model could be trained using modern cloud infrastructure. He fit a hierarchical logistic regression with close to two million parameters, using PyMC's Hamiltonian Monte Carlo on a single A100 GPU rented through Modal, a serverless platform that deploys a Python script straight to GPU hardware with almost no setup. He'd originally guessed it would cost around five dollars, the price of a Big Mac, but the real bill came in an order of magnitude lower. A model that would take a Gibbs sampler weeks to run, and that once required a research lab's dedicated GPU, now costs pocket change and a few minutes of setup.
Q: What's the current bottleneck in Bayesian-at-scale tooling?
A: Chris argues the software has largely caught up: PyMC's JAX backend and NumPyro make GPU-accelerated Bayesian modeling work out of the box for most problems. What's missing is common knowledge. Companies are clearly running large Bayesian models in production, but the results stay behind corporate firewalls. Chris’ proposal is a community benchmark effort: which frameworks handle a million-parameter Markov random field on a given GPU out of the box, since this kind of expensive, slow-running benchmark is a poor fit for standard CI pipelines but valuable for the field to know.
Chapters:
22:57 When does GPU acceleration actually pay off for a Bayesian model?
26:33 What did it cost to train a two-million-parameter model on Modal?
30:36 What happened when Chris asked 200 different LLMs to flip a coin?
34:50 Where do Bayesian ideas show up in the agentic AI systems Chris builds at Nvidia?
40:16 Are statisticians being made obsolete by large language models?
41:19 How does putting a Gaussian process on unknown coordinates fix noisy data in mineral prospecting?
58:05 What is Chris looking forward to working on next?
Thank you to my Patrons for making this episode possible!
Links from the show here
View more
