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:
“I thank you, Father, Lord of heaven and earth, because you have hidden these things from the wise and the intelligent and have revealed them to infants...”
—Bible: New Testament, Matthew 11:25.
“The best way to teach a child restraint and generosity is to be a model of those qualities yourself. If your child sees that you want a particular item but refrain from buying it, either because it isn’t practical or because you can’t afford it, he will begin to understand restraint. Likewise, if you donate books or clothing to charity, take him with you to distribute the items to teach him about generosity.”
—Lawrence Balter (20th century)