Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains.
I've come across time-series models, HMMs, and Kalman filters independently. But the language used seems to be somewhat "siloed" in the sense that it's not obvious how these models are related in a broader sense. From the wiki quote above, Dynamic Bayesian Networks unify these different models into a cohesive philosophy (for lack of a better word.)
This sounds great! But I'd really like a textbook or online course to help me get up to speed. Could anyone link such a resource?