In biology, PGMs were one of the first successful forms of "machine learning"- given a large set of examples, train a graphical model using probabilities using EM, and then pass many more examples through the model for classification. The HMM for proteins is pretty straightforward, basically just a probabilistic extension of using dynamic programming to do string alignment.
My perspective- which is a massive simplification- is that sequence models are a form of graphical model, although the graphs tend to be fairly "linear" and the predictions generate sequences (lists) rather than trees or graphs.
In biology, PGMs were one of the first successful forms of "machine learning"- given a large set of examples, train a graphical model using probabilities using EM, and then pass many more examples through the model for classification. The HMM for proteins is pretty straightforward, basically just a probabilistic extension of using dynamic programming to do string alignment.
My perspective- which is a massive simplification- is that sequence models are a form of graphical model, although the graphs tend to be fairly "linear" and the predictions generate sequences (lists) rather than trees or graphs.