I was talking about things inspired by (for example) hidden markov models. See https://en.wikipedia.org/wiki/Graphical_model https://en.wikipedia.org/wiki/Graph

I was talking about things inspired by (for example) hidden markov models. See https://en.wikipedia.org/wiki/Graphical_model https://en.wikipedia.org/wiki/Graphical_model 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.