Bayesian networks (BNs) are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, I will discuss how BNs can be extended to model continuous data and data in which observations are not independent and identically distributed.
For the former, I will discuss continuous-time BNs. For the latter, I will show how mixed effects models can be integrated with BNs to get the best of both worlds.