Probabilistic graphical models are widely used to model systems. However, for applications with inherent asymmetries or context-specific independences, standard methods may not be the most useful. There are now several classes of broader models that can be used to capture these semantics. These models include chain graphs, staged trees, chain event graphs, and certain hidden Markov models. This meeting includes subtracks on the particular impact of these models on categorical data, causal inference, dynamic variants, and applications. Attendees include academic statisticians and postgraduate students.