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)
“The chess-board is the world; the pieces are the phenomena of the universe; the rules of the game are what we call the laws of Nature. The player on the other side is hidden from us. We know that his play is always fair, just, and patient. But also we know, to our cost, that he never overlooks a mistake, or makes the smallest allowance for ignorance.”
—Thomas Henry Huxley (1825–1895)
“Socrates, who was a perfect model in all great qualities, ... hit on a body and face so ugly and so incongruous with the beauty of his soul, he who was so madly in love with beauty.”
—Michel de Montaigne (1533–1592)