I use graphical models all over the place, typically for problems that have more structure than simple statistics calculations, but don't need the huge capacity of machine learning models. For example I work with bio folks measuring "EC50" values, basically parameters of titration curves in a wet lab. It seems like a simple curve fitting problem with say 5 parameters. But then these wet lab scientists measure hundreds of curves at once, so we want to put hierarchical structure among the curves -- that is all the curves should look pretty similar with only a few degrees of freedom. Graphical models are a great framework for expressing prior knowledge about the dependencies between these curve parameters. But yeah I then do inference in graphical models using variational inference in PyTorch.