Hawkes Process Inference With Missing Data
Hawkes Process Inference With Missing Data
复制标题
Hawkes 使用缺失数据进行推理
DOI：
10.1609/aaai.v32i1.12116
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Chandini Shetty
中科院分区：
文献类型：
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作者：
C. Shelton;Zhen Qin;Chandini Shetty
多变量霍克斯过程是一类标记点过程：样本由无限随机大小的有限事件集组成;每个事件都有一个实值时间和一个离散值标签（标记）。它是自我兴奋的：每个事件都会导致（不久的）将来其他事件（相同或不同标签）的发生率增加。先前的工作已经开发了从完整样本中进行参数估计的方法。然而，正如未观察到的变量可以增加其他概率模型的建模能力一样，允许未观察到的事件可以增加点过程的建模能力。在本文中，我们开发了一种方法，在多元Hawkes过程中的未观察到的事件的后验分布的采样。我们证明了我们的方法的有效性，以及它在提高预测能力和识别真实数据中的潜在结构方面的实用性。
A multivariate Hawkes process is a class of marked point processes: A sample consists of a finite set of events of unbounded random size; each event has a real-valued time and a discrete-valued label (mark). It is self-excitatory: Each event causes an increase in the rate of other events (of either the same or a different label) in the (near) future. Prior work has developed methods for parameter estimation from complete samples. However, just as unobserved variables can increase the modeling power of other probabilistic models, allowing unobserved events can increase the modeling power of point processes. In this paper we develop a method to sample over the posterior distribution of unobserved events in a multivariate Hawkes process. We demonstrate the efficacy of our approach, and its utility in improving predictive power and identifying latent structure in real-world data.
DOI：
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发表时间：
2013-11
期刊：
Advances in neural information processing systems
影响因子：
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作者：
Nan Du;Le Song;M. Gomez-Rodriguez;H. Zha
通讯作者：
Nan Du;Le Song;M. Gomez-Rodriguez;H. Zha