Poverty Bayes: fitting million-parameter models for pennies with serverless MCMC
The article discusses advancements in Bayesian statistics facilitated by the deep learning revolution, particularly through the use of serverless MCMC methods. It highlights the ease of accessing powerful GPU resources for large-scale modeling, specifically using hierarchical logistic regression. The author shares a workflow for implementing these models using synthetic data and modern programming tools.
- ▪The deep learning revolution has improved computational efficiency for applied probabilists.
- ▪User-friendly platforms for renting GPUs are becoming increasingly available.
- ▪The article presents a workflow for using GPU-based inference on Modal for large Bayesian models.
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Record
| Original publisher | Christopherkrapu |
| Canonical URL | https://christopherkrapu.com/blog/2026/poverty-bayes-serverless-mcmc/ |
| Publication time | Wed, 27 May 2026 05:16:41 +0000 |
| Retrieval time | 2026-05-27T05:37:56.834Z |
| Last seen | 2026-05-27T05:37:56.834Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | c4s74nXNEUUu |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
It’s a good time to be an applied probabilist. The deep learning revolution has led to tremendous improvements in the $ / flops department, and we Bayesians can easily hop on this train! During grad school, I used to spend nights and weekends babysitting MCMC runs on my GeForce Titan XP running in my bedroom (by the way, thank you NVIDIA Academic Grant Program) while simultaneously trying to keep the waste heat from cooking me as I slept. If you are a newcomer to this field, rejoice in the knowledge that all this suffering is a thing of the past. A slew of companies are rushing to the fore with user-friendly platforms for renting GPUs. For prototyping, I really enjoy working with Modal since I’m cheap and I’m too lazy to keep managing my own fleet.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Christopherkrapu.
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