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About H2M

H2M is a set of MATLAB/OCTAVE functions that implement the EM algorithm [1], [2] in the case of mixture models or hidden Markov models with multivariate Gaussian state-conditional distribution. More specifically, three special cases have been considered
  1. Gaussian mixture models.
  2. Ergodic (or fully connected) Gaussian hidden Markov models.
  3. Left-right Gaussian hidden Markov models.
In fact, the case 2 and 3 above do not significantly differ except for the fact that in the case of a left-right HMM, one needs to estimate the parameters from multiple observation sequences. In all three cases, it is possible to use either diagonal or full covariance matrices for the state-conditional distributions.

The H2M/cnt extension (added in version 1.6) handles similar models but for scalar count (discrete valued positive) data. Three cases have been considered

  1. Mixture of Poisson distributions.
  2. Hidden Markov models with Poisson state conditional distribution.
  3. Hidden Markov models with Negative binomial state conditional distribution.
Compared to the main H2M functions, only the case of ergodic models (ie. models that can be trained from a single long observation sequence rather than from multiple sequences) has been considered.



Olivier Cappé, Aug 24 2001