From a [similar Cross Validation question][1] follows [@jerad][2] answer:

> HMMs are not equivalent to DBNs, rather they are a special case of DBNs in which the entire state of the world is represented by a single hidden state variable. Other models within the DBN framework generalize the basic HMM, allowing for more hidden state variables (see the second paper above for the many varieties).

>Finally, no, DBNs are not always discrete. For example, linear Gaussian state models (Kalman Filters) can be conceived of as continuous valued HMMs, often used to track objects in space.

>I'd recommend looking through these two excellent review papers:

>* [An Introduction to Hidden Markov Models and Bayesian Networks by Zoubin Gharamani][3]
>* [Dynamic Bayesian Networks by Kevin Murphy][4]

(I usually would post this as a comment but I still haven't rep to do so).

  [1]: https://stats.stackexchange.com/questions/44702/definition-of-dynamic-bayesian-system-and-its-relation-to-hmm
  [2]: https://stats.stackexchange.com/users/17096/jerad
  [3]: http://select.cs.cmu.edu/class/10701-F09/readings/zoubin-hmms.pdf
  [4]: http://www.cs.ubc.ca/~murphyk/Papers/dbnchapter.pdf