The hidden Markov model

The hidden Markov model has numerous applications related to speech recognition, face identification (biometrics), and pattern recognition in pictures and videos [7:3].

A hidden Markov model consists of a Markov process (also known as a Markov chain) for observations with a discrete time. The main difference with the Markov processes is that the states are not observable. A new observation is emitted with a probability known as the emission probability each time the state of the system or model changes.

There are two sources of randomness, which are as follows:

  • Transition between states
  • Emission of an observation when a state is given

Let's reuse the boxes and balls example. If the boxes are hidden states (nonobservable), then ...

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