Abstract
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides provable robustness by leveraging the generalised Bayesian perspective, and also addresses the scalability issues of previous attempts. Specifically, the proposed generalised Bayesian formalism leads to conjugate posteriors whose parameters are available in closed form by leveraging diffusion score matching. The resulting algorithm is exact, can be updated through simple algebra, and is more than 10 times faster than its closest competitor.
Jeremias Knoblauch
Associate Professor and EPSRC Fellow in Machine Learning & Statistics
My research interests include robust Bayesian methods, generalised and post-Bayesian methodology, variational methods, and simulators.