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.
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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