Stein Mixture Filtering: Advancing Bayesian Data Science Towards Real-Time Inference

Real-time Bayesian inference remains difficult in the non-Gaussian, high-dimensional settings where data science increasingly operates. Particle filters degenerate as dimension grows, while standard variational filters impose Gaussian assumptions that misrepresent a system's true uncertainty.

This project develops variational mixture filtering to address this, fitting mixture variational families that capture non-Gaussian, multimodal posteriors while remaining cheap enough to update in real time.

It will deliver theory for the convergence of mixture flows in variational filtering, scalable filtering methods, and open-source implementations for real-time Bayesian data science.