  One of the central tasks in many-body physics is the determination of phase
diagrams. However, mapping out a phase diagram generally requires a great deal
of human intuition and understanding. To automate this process, one can frame
it as a classification task. Typically, classification problems are tackled
using discriminative classifiers that explicitly model the probability of the
labels for a given sample. Here we show that phase-classification problems are
naturally suitable to be solved using generative classifiers based on
probabilistic models of the measurement statistics underlying the physical
system. Such a generative approach benefits from modeling concepts native to
the realm of statistical and quantum physics, as well as recent advances in
machine learning. This leads to a powerful framework for the autonomous
determination of phase diagrams with little to no human supervision that we
showcase in applications to classical equilibrium systems and quantum ground
states.
