The Bayesian two-step change point detection method is popular for the Hawkes process due to its simplicity and intuitiveness. However, the non-conjugacy between the point process likelihood and the prior requires most existing Bayesian two-step change point detection methods to rely on non-conjugate inference methods. These methods lack analytical expressions, leading to low computational efficiency and impeding timely change point detection. To address this issue, this work employs data augmentation to propose a conjugate Bayesian two-step change point detection method for the Hawkes process, which proves to be more accurate and efficient. Extensive experiments on both synthetic and real data demonstrate the superior effectiveness and efficiency of our method compared to baseline methods. Additionally, we conduct ablation studies to explore the robustness of our method concerning various hyperparameters. Our code is publicly available at https://github.com/Aurora2050/CoBay-CPD.

翻译：贝叶斯两步变点检测方法因其简洁性和直观性，在霍克斯过程分析中广受欢迎。然而，点过程似然函数与先验分布之间的非共轭性导致现有大多数贝叶斯两步变点检测方法必须依赖非共轭推断方法。这类方法缺乏解析表达式，导致计算效率低下，阻碍了及时的变点检测。为解决这一问题，本研究通过数据增强技术，提出了一种适用于霍克斯过程的共轭贝叶斯两步变点检测方法，该方法被证明具有更高的准确性和效率。在合成数据与真实数据上的大量实验表明，相较于基线方法，本方法在效能与效率方面均表现出显著优势。此外，我们通过消融实验探究了本方法对不同超参数的鲁棒性。相关代码已公开于 https://github.com/Aurora2050/CoBay-CPD。