Assuming an undirected graphical model G over an observed entity x and a set of discrete, interdependent random variables 4 y, a conditional random field describes the conditional distribution: The normalization term Z(x) sums over all possible joint outcomes of y, i.e.,  In practice, for efficiency reasons, independence assumptions have to be made about variables y ∈ y, so G is restricted to small cliques (say, (|c| ≤ 3).
the sufficient statistics only depend on a limited number of variables y c ⊆ y; they can, however, access the whole observed entity x.