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In this paper we investigate the use of staged tree models for discretelongitudinal data. Staged trees are a type of probabilistic graphical model forfinite sample space processes. They are a natural fit for longitudinal databecause a temporal ordering is often implicitly assumed and standard methodscan be used for model selection and probability estimation. However, modelselection methods perform poorly when the sample size is small relative to thesize of the graph and model interpretation is tricky with larger graphs. Thisis exacerbated by longitudinal data which is characterised by repeatedobservations. To address these issues we propose two approaches: thelongitudinal staged tree with Markov assumptions which makes some initialconditional independence assumptions represented by a directed acyclic graphand marginal longitudinal staged trees which model certain margins of the data.

Staged trees for discrete longitudinal data