Since BNs and CRFs are here parameterized as log-linear models and rely on the same set of feature functions, a second approach is to use CRF-trained model parameters to build a BN classifier that assigns a probability to each y t .
the CRF model is first used to generate label predicitonsŷ, from which the locally-normalized model estimates the cost of predictingŷ t = 1 given a label historyŷ 1:t−1 .