Summary of Conjugate Bayesian Two-step Change Point Detection For Hawkes Process, by Zeyue Zhang et al.
Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
by Zeyue Zhang, Xiaoling Lu, Feng Zhou
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Bayesian two-step change point detection method for the Hawkes process is a popular approach due to its simplicity and intuitiveness. However, most existing methods rely on non-conjugate inference methods that lack analytical expressions, leading to low computational efficiency and impeding timely change point detection. This paper addresses this issue by employing 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. The proposed method is evaluated on both synthetic and real data, demonstrating its superior effectiveness and efficiency compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to detect changes in patterns of events over time, using a type of mathematical model called the Hawkes process. This method is important because it can be used to analyze large datasets quickly and accurately, which is useful for many applications, such as detecting anomalies in financial transactions or identifying changes in weather patterns. |
Keywords
» Artificial intelligence » Data augmentation » Inference
