We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with $\beta$-divergences. The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as $\beta \to 0$. Secondly, we give a principled way of choosing the divergence parameter $\beta$ by minimizing expected predictive loss on-line.

We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with \beta -divergences. The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as \beta \to 0 . Secondly, we give a principled way of choosing the divergence parameter \beta by minimizing expected predictive loss on-line.

Overview The paper introduces a robust online change point detection algorithm for non-stationary time-series data. Robustness comes as a by product of minimizing \beta-divergence between data and fitted model as opposed to using KL divergence as in standard Bayesian inference. In the generalized Bayesian inference the posteriors are intractable. The paper mitigate this problem by resorting to structural variational approximation, which is proved to be exact as \beta converges to zero. The paper also discusses systematic approaches to initialize \beta and refine it online.

We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with $\beta$-divergences. The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as $\beta \to 0$. Secondly, we give a principled way of choosing the divergence parameter $\beta$ by minimizing expected predictive loss on-line.