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Line 337: f_curr[st] = e[st][x_i] * prev_f_sum ~~sum_prob = sum(f_curr.values())~~ ~~for st in states:~~ ~~f_curr[st] /= sum_prob # normalising to make sum == 1~~ fwd.append(f_curr) f_prev = f_curr

Forward–backward algorithm: Difference between revisions