Beyond decision detection, DGMs are used for labeling and segmenting sequences of observations in many different fields-including bioinformatics, ASR, Natural Language Processing (NLP), and information extraction.
dynamic Bayesian Networks (dBNs) are a popular model for probabilistic sequence modeling because they exploit structure in the problem to compactly represent distributions over multi-state and observation variables.