Given the model parameters θ , the hidden state values of sequence Y are computed using the Viterbi inference algorithm used in HMMs.
the trained logistic regression model only requires a model parameter matrix W in the size of L × (2L + V + S + 1) to calculate the probability P(Z t |Z t−1 , Z t+1 , X t , Y t , W ) for any inputs.