A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov. This means that the probability of there being a change in the hidden state depends on the amount of time that has elapsed since entry into the current state. This is in contrast to hidden Markov models where there is a constant probability of changing state given survival in the state up to that time.
For instance Sansom et al. modelled daily rainfall using a hidden semi-Markov model. If the underlying process (e.g. weather system) does not have a geometrically distributed duration, an HSMM may be more appropriate.
Statistical inference for hidden semi-Markov models is more difficult than in hidden Markov models, since algorithms like the Baum-Welch algorithm are not directly applicable, and must be adapted requiring more resources.
Famous quotes containing the words hidden and/or model:
“Impenetrable in their dissimulation, cruel in their vengeance, tenacious in their purposes, unscrupulous as to their methods, animated by profound and hidden hatred for the tyranny of man—it is as though there exists among them an ever-present conspiracy toward domination, a sort of alliance like that subsisting among the priests of every country.”
—Denis Diderot (1713–1784)
“...that absolutely everything beloved and cherished of the bourgeoisie, the conservative, the cowardly, and the impotent—the State, family life, secular art and science—was consciously or unconsciously hostile to the religious idea, to the Church, whose innate tendency and permanent aim was the dissolution of all existing worldly orders, and the reconstitution of society after the model of the ideal, the communistic City of God.”
—Thomas Mann (1875–1955)