We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes. The framework can handle both exchangeable and non-exchangeable event sequences with minimal tuning and without any pre-training. This contrasts with many parametric and non-parametric state-of-the-art methods that typically require pre-training and/or careful tuning, and can only handle exchangeable event sequences. The framework's competitive computational and predictive performance against other state-of-the-art methods are illustrated through real data experiments. Its attractiveness for large-scale applications is demonstrated through a case study involving all events occurring in an English Premier League season.

翻译：我们采用受霍克斯过程启发的参数化条件概率质量函数以保持标记的可解释性，并应用变分推断技术推导出适用于标记点过程的通用可扩展推断框架。该框架只需最少调参且无需预训练，即可处理可交换与不可交换事件序列。这与许多需要预训练和/或精细调参、且仅能处理可交换事件序列的先进参数与非参数方法形成鲜明对比。通过真实数据实验，该框架在计算效率与预测性能方面与其他先进方法相比具有竞争力。基于某个英格兰足球超级联赛赛季全量事件的案例研究，进一步验证了其在大规模应用中的吸引力。