Two types of density model
(with hidden configurations h)
      Stochastic generative model
using directed acyclic graph
(e.g. Bayes Net)
Generation from model is easy
Inference can be hard
Learning is easy after inference
    Energy-based models that
associate an energy with each data
vector + hidden configuration
Generation from model is hard
Inference can be easy
Is learning hard?