Synonyms
Definition
Hidden Markov models (HMMs) form a class of statistical models in which the system being modeled is assumed to be a Markov process with hidden states. From observed output sequences generated by the Markov process, both the output emission probabilities from the hidden states and the transition probabilities between the hidden states can be estimated by using dynamic programming methods. The estimated model parameters can then be used for various sequence analysis purposes.
Motivation and Background
The states of a regular Markov model, named after Russian mathematician Andrey Markov (1865–1922), are directly observable, hence its only parameters are the state transition probabilities. In many real-world cases, however, the states of the system that one wants to model are not directly observable. For instance, in speech recognition the audio is the observable stream, while the goal is to discover the phonemes (the categorical elements of speech) that emitted the...
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Recommended Reading
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Bosch, A. (2011). Hidden Markov Models. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_362
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DOI: https://doi.org/10.1007/978-0-387-30164-8_362
Publisher Name: Springer, Boston, MA
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