In this paper, we perform a sparse filtering recursion for efficient changepoint detection for discrete-time observations. We attach auxiliary event times to the chronologically ordered observations and formulate multiple changepoint problems of discrete-time observations into continuous-time observations. Ideally, both the computational and memory costs of the proposed auxiliary uniformisation forward-filtering backward-sampling algorithm can be quadratically scaled down to the number of changepoints instead of the number of observations, which would otherwise be prohibitive for a long sequence of observations. To avoid model bias, a time-varying changepoint recurrence rate across different segments is assumed to characterise diverse scales of run lengths of the changepoints. We demonstrate the methods through simulation studies and real data analysis.

中文翻译：

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通过辅助均匀化进行可扩展的贝叶斯多变点检测

在本文中，我们执行稀疏过滤递归以对离散时间观测进行有效的变点检测。我们将辅助事件时间附加到按时间顺序排列的观察，并将离散时间观察的多个变点问题转化为连续时间观察。理想情况下，所提出的辅助均匀化前向过滤后向采样算法的计算和内存成本都可以按比例缩小到变化点的数量而不是观察的数量，否则这对于长序列的观察来说是令人望而却步的。为了避免模型偏差，假定跨不同段的时变变化点重复率来表征变化点运行长度的不同尺度。