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?