  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.
