Dynamic Bayesian network

DBNs were developed by Paul Dagum in the early 1990s at Stanford University's Section on Medical Informatics.

[3][4] Today, DBNs are common in robotics, and have shown potential for a wide range of data mining applications.

For example, they have been used in speech recognition, digital forensics, protein sequencing, and bioinformatics.

DBN is a generalization of hidden Markov models and Kalman filters.

[5] DBNs are conceptually related to probabilistic Boolean networks[6] and can, similarly, be used to model dynamical systems at steady-state.

Dynamic Bayesian Network composed by 3 variables.
Bayesian Network developed on 3 time steps.
Simplified Dynamic Bayesian Network. All the variables do not need to be duplicated in the graphical model, but they are dynamic, too.