In principle, given an observation sequence , the goal of a conditional probability model is to find a stochastic optimal By applying the Bayes' rule, we can rewrite the equation (1) as: Obviously, the second term MI captures the mutual information between the state sequence and the observation sequence o .
to compute efficiently, we propose a novel mutual information independence assumption: we assume a state is only dependent on the observation sequence o and independent on other states in the state sequence s .