Hidden Markov Models and the Viterbi algorithm
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چکیده
is understood to have N hidden Markov states labelled by i (1 ≤ i ≤ N), and M possible observables for each state, labelled by a (1 ≤ a ≤ M). The state transition probabilies are pij = p(qt+1 = j | qt = i), 1 ≤ i, j ≤ N (where qt is the hidden state at time t), the emission probability for the observable a from state i is ei(a) = p(Ot = a | qt = i) (where Ot is the observation at time t), and the initial state probabilities are wi = p(q1 = i). Given a sequence of observations O = O1 O2 · · ·OT , and an HMMH = (
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