4 ms·
I still have yet to use graphical models (in the traditional sense, not including the new age variational inference style neural networks as graphical models) i
by eachro 4y ago
I still have yet to use graphical models (in the traditional sense, not including the new age variational inference style neural networks as graphical models) in real life. Am I just completely missing something? Where do people generally find compelling uses for graphical models?
- fritzo 4y agoI 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.
- gmquestion 4y agoIs there a resource you would recommend for learning about applying GM and related techniques to data like those you described (similar structures with N DoF)? As a hobbyist I've been toying with symbolic regression and this feels like a wall I've been running up against
- yellowcake0 4y agoThey are used quite a bit in computational genomics. Indeed, genomics is full of latent variable problems where one has a good model for the underlying phenomena but often not a lot of labeled data.