A hierarchical hidden Markov model (HMM) is a probabilistic model in which the probability of each possible next state is determined by the state and Markov chain that precedes it, as well as by the probability of the previous state.
This allows the model to be learned from data, without needing to specify beforehand what the probabilities should be.
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