A hidden Markov model (HMM) is a statistical model where the system being modelled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters of the Markov model based on this assumption.
The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications.
Applications of hidden Markov models:
- speech recognition
- natural language understanding
- genomics
- and many more...
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