Great to see the seroanalytics.org collection expanding with @dchodge.bsky.social 's work on seroCOP (R package for analysing correlates of protection using Bayesian methods)
It serves as a nice reminder to me that some of the most elegant solutions in machine learning and statistics aren't always the newest deep learning architectures. Sometimes, good old Bayesian inference with smart sampling strategies can create something really cool!
- Proposals are accepted/rejected based on how well they reconstruct your image
- The final painting emerges from thousands of probabilistic decisions
- It's not deterministic, run it twice -> get two different artworks
Upload an image, and the algorithms "paints", one probabilistic brushstroke at a time. Each brushstroke represents a "birth" or "death" jump in the model space:
- The algorithm proposes adding new strokes or removing existing ones
I built an MCMC painter!
I'm excited to share this project I've been working on for a long time, which sits at the intersection of computational statistics and generative art; mcmcPainter!
Link here: mcmcpainter.davidhodgson.me