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:我们采用参数性、霍克斯过程激励的有条件概率质量功能为标记提供的解释性功能,并采用变推法,为标记点过程制定一般和可伸缩的推断性框架,该框架可以处理可交换和非交换事件序列,进行最低限度的调试,不经过任何预先培训,这与许多参数性和非参数性的最新方法形成对照,这些方法通常需要预先培训和/或仔细调整,并且只能处理可交换事件序列。框架与其他最先进的方法相比具有竞争性的计算和预测性性业绩通过实际数据试验加以说明,它对于大规模应用的吸引力通过涉及英国总理联盟季节发生的所有事件的案例研究得到证明。