/HiddenMarkovModel_TensorFlow

TensorFlow: Viterbi, Forward-Backward and Baum Welch with a Hidden Markov Model (HMM)

Primary LanguageJupyter Notebook

Hidden Markov Model in TensorFlow

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Viterbi Algorithm

  • Efficient way of finding the most likely state sequence.
  • Method is general statistical framework of compound decision theory.
  • Maximizes a posteriori probability recursively.
  • Assumed to have a finite-state discrete-time Markov process.

Forward-Backward Algorithm

  • The goal of the forward-backward algorithm is to find the conditional distribution over hidden states given the data.
  • It is used to find the most likely state for any point in time.
  • It cannot, however, be used to find the most likely sequence of states (see Viterbi)

Baum Welch Algorithm

  • Expectation Maximization Inference of unknown parameters of a Hidden Markov Model.

Viterbi

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Belief Propagation

Backtrack

Baum Welch and Forward-Backward

Forward-Backward

Re-estimate

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