We present a simple reduction from sequential estimation to sequential changepoint detection (SCD). In short, suppose we are interested in detecting changepoints in some parameter or functional $\theta$ of the underlying distribution. We demonstrate that if we can construct a confidence sequence (CS) for $\theta$, then we can also successfully perform SCD for $\theta$. This is accomplished by checking if two CSs -- one forwards and the other backwards -- ever fail to intersect. Since the literature on CSs has been rapidly evolving recently, the reduction provided in this paper immediately solves several old and new change detection problems. Further, our "backward CS", constructed by reversing time, is new and potentially of independent interest. We provide strong nonasymptotic guarantees on the frequency of false alarms and detection delay, and demonstrate numerical effectiveness on several problems.

翻译：简而言之,如果我们有兴趣发现某些参数的变化点或基本分布的功能值$=美元,那么我们就可以简单地从顺序估算到顺序变化点检测(SCD),我们就可以简单地从顺序估算到顺序变化点检测(SCD)。我们证明,如果我们能为美元构建信任序列(CS),那么我们也可以成功地以美元实现SCD。我们通过检查两个 CS(一个向前,另一个向后)是否从未发生交叉,就可以做到这一点。由于关于 CS 的文献最近迅速发展,本文提供的削减立即解决了几个新老变化检测问题。此外,我们通过倒转时间构建的“后向 CS”是新的,可能具有独立的兴趣。我们对错误警报和检测延迟的频率提供了强有力的非同步保证,并展示了几个问题的数字有效性。