Change-point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large-scale, high-dimensional, and complex streaming data call for computationally (memory) efficient sequential change-point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade-off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change-point detection procedures, ranging from classic sequential change-point detection algorithms to more recent non-parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which still provides useful techniques for analyzing new procedures.

翻译：变点检测研究的是在数据流底层分布发生变化后，如何尽快检测到该变化的问题。现代大规模、高维、复杂的流式数据要求设计既具有统计功效，又具备计算（内存）高效的序贯变点检测算法。这引发了计算效率与统计能力之间的权衡，这一方面在以往经典文献中较少被强调。本教程从这一新视角出发，回顾了多种序贯变点检测方法，涵盖从经典序贯变点检测算法到近期考虑计算、内存效率及模型鲁棒性的非参数化方法。我们的综述亦包含经典性能分析，这些分析仍可为新方法的分析提供有用的技术支撑。