Hierarchical Hidden Markov Model
The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM). In an HHMM each state is considered to be a self-contained probabilistic model. More precisely each state of the HHMM is itself an HHMM.
HHMMs and HMMs are useful in many fields, including pattern recognition.
Read more about Hierarchical Hidden Markov Model: Background, The Hierarchical Hidden Markov Model
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