layout of the graph of random variables Y can be arbitrary
relax certain assumptions
An HMM can loosely be understood as a CRF with very specific feature functions that use constant probabilities to model state transitions and emissions
feature functions can inspect the entire input sequence X at any point during inference
in contrast to HMMs, CRFs can contain any number of feature functions