Class MixtureHMMEM

All Implemented Interfaces:
RandomNumberGenerator, Seedable

public class MixtureHMMEM extends MixtureHMM
The EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm. It does the following.
  • the E step: Compute the conditional expectations given the observations and the current estimates of the parameters.
  • the M step: Maximize, with respect to the estimated parameters and the data set, log-likelihood.
These two steps are repeated until some convergence criterion has been satisfied.
See Also:
    • W. Zucchini and I. L. MacDonald, "Hidden Markov Models for Time Series: An Introduction Using R," Boca Raton, Florida, CRC Press, 2009.
    • L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, Volume: 77, Issue:2, 257 - 286, Feb 1989."
  • Constructor Details

    • MixtureHMMEM

      public MixtureHMMEM(double[] observations, MixtureHMM model0, double epsilon, int maxIterations)
      Constructs a mixture HMM model by training an initial model using the Baum-Welch algorithm.
      observations - the observations
      model0 - an initial model
      epsilon - a precision parameter: when a number |x| ≤ ε, it is considered 0
      maxIterations - the maximum number of iterations
  • Method Details

    • train

      public static MixtureHMMEM.TrainedModel train(MixtureHMM model0, double[] observations)
      Constructs a trained mixture hidden Markov model, one iteration.
      model0 - the initial hidden Markov model
      observations - the observations
      a trained mixture hidden Markov model