  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.
