I took the online course of probabilistic graphical models (PGM) online from Daphne Koller and Eric Xing's videos, but I do not think I get the main idea of PGM, and I would appreciate it if someone can clarify some of my questions on the usefulness and methodology of PGM.
- What are some fields that PGM performs better than other models? I think it was the mainstream model in computer vision before deep learning took its place, and I saw many application in Daphne Koller's class materials in biological/medical domain. Is there any other field that PGM is popular?
- Is PGM ever useful for tabular data? I never recall PGM ever wins any Kaggle contest.
- Is there a general methodology of modeling using PGM? I feel I always need to write the full joint distribution of all observable/hidden variables, then use the inference/learning algorithm to get the parameters and predictions. If that is the case, then does it mean PGM can only be applied to the fields where I know pretty much everything about the all variables, so it is only useful in the fields where the prior knowledge is strong such as computer vision and biological/medical data?
- If PGM has to be modeled in a white box way, does that mean I cannot use it as a blackbox model to discover the patterns I cannot find by hand?
- What is special about the graph in PGM? I feel they only exist to allow fast identification of conditional independent variables, in order to allow fast inference/learning. In other words, the graph is like a visualization of the model. Then why do we care so much about the graph?