论文标题
时间尺度图形事件模型的可学习性
Learnability of Timescale Graphical Event Models
论文作者
论文摘要
该技术报告试图填补有关时间尺度图形事件模型的当前文献中的空白。我建议和评估不同的启发式方法，以确定结构学习算法的超参数并完善现有的距离度量。将对合成数据进行全面的基准，以结论不同启发式方法的适用性。
This technical report tries to fill a gap in current literature on Timescale Graphical Event Models. I propose and evaluate different heuristics to determine hyper-parameters during the structure learning algorithm and refine an existing distance measure. A comprehensive benchmark on synthetic data will be conducted allowing conclusions about the applicability of the different heuristics.
