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
Famous quotes containing the words hierarchical, hidden and/or model:
“Authority is the spiritual dimension of power because it depends upon faith in a system of meaning that decrees the necessity of the hierarchical order and so provides for the unity of imperative control.”
—Shoshana Zuboff (b. 1951)
“What has been hidden by snow is revealed by a thaw.”
—Swedish proverb, trans. by Verne Moberg.
“There are very many characteristics which go into making a model civil servant. Prominent among them are probity, industry, good sense, good habits, good temper, patience, order, courtesy, tact, self-reliance, many deference to superior officers, and many consideration for inferiors.”
—Chester A. Arthur (1829–1886)