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.

翻译：本文提出了一种在线、可证明鲁棒且可扩展的贝叶斯变点检测方法。该算法相较于先前工作具有关键优势：通过利用广义贝叶斯视角实现了可证明的鲁棒性，同时解决了先前方法中存在的可扩展性问题。具体而言，所提出的广义贝叶斯框架通过扩散分数匹配生成了共轭后验分布，其参数可通过封闭形式直接获得。该算法具有精确性，可通过简单代数运算进行更新，且运算速度比性能最接近的竞争者快十倍以上。