Machine Learning — Hidden Markov Model (HMM)

Markov process/Markov chains

Hidden Markov Model (HMM)

  • It includes the initial state distribution π (the probability distribution of the initial state)
  • The transition probabilities A from one state (xt) to another.
  • HMM also contains the likelihood B of the observation (yt) given a hidden state. Matrix B is called the emission probabilities. It demonstrates the probability of our observation given a specific internal state.
  • Likelihood: How likely are the observations based on the current model or the probability of being at a state at a specific time step.
  • Decoding: Find the internal state sequence based on the current model and observations.
  • Learning. Learn the HMM model.
Modified from source



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