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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN"><!--Converted with LaTeX2HTML 2K.1beta (1.48)original version by: Nikos Drakos, CBLU, University of Leeds* revised and updated by: Marcus Hennecke, Ross Moore, Herb Swan* with significant contributions from: Jens Lippmann, Marek Rouchal, Martin Wilck and others --><HTML><HEAD><TITLE>About H2M</TITLE><META NAME="description" CONTENT="About H2M"><META NAME="keywords" CONTENT="H2M, H2M/cnt, Hidden Markov Model, HMM, Mixture model, Vector Quantization, Expectation Maximization, EM, Multivariate Gaussian, Count data, Poisson, Negative binomial, MATLAB, OCTAVE, GPL"><META NAME="resource-type" CONTENT="document"><META NAME="distribution" CONTENT="global"><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso-8859-1"><META NAME="Generator" CONTENT="LaTeX2HTML v2K.1beta"><META HTTP-EQUIV="Content-Style-Type" CONTENT="text/css"><LINK REL="STYLESHEET" HREF="h2m.css"><LINK REL="next" HREF="node4.html"><LINK REL="previous" HREF="node2.html"><LINK REL="up" HREF="node2.html"><LINK REL="next" HREF="node4.html"></HEAD><BODY BGCOLOR="ivory"><!--Navigation Panel--><B> Next:</B> <A NAME="tex2html112" HREF="node4.html">Current version and changes</A><B>Up:</B> <A NAME="tex2html108" HREF="node2.html">Introduction</A><B> Previous:</B> <A NAME="tex2html102" HREF="node2.html">Introduction</A><P><!--End of Navigation Panel--><H2><A NAME="SECTION00021000000000000000">About <TT>H2M</TT></A></H2><TT>H2M</TT> is a set of MATLAB/OCTAVE functions that implement the EM algorithm[<A HREF="node23.html#Dempster:EM">1</A>], [<A HREF="node23.html#Wu:EM">2</A>] in the case of mixture models or hidden Markovmodels with multivariate Gaussian state-conditional distribution. Morespecifically, three special cases have been considered<OL><LI>Gaussian mixture models.</LI><LI>Ergodic (or fully connected) Gaussian hidden Markov models.</LI><LI>Left-right Gaussian hidden Markov models.</LI></OL>In fact, the case 2 and 3 above do not significantly differ except for the factthat in the case of a left-right HMM, one needs to estimate the parameters frommultiple observation sequences. In all three cases, it is possible to useeither diagonal or full covariance matrices for the state-conditionaldistributions.<P>The <TT>H2M/cnt</TT> extension (added in version 1.6) handles similar models but forscalar count (discrete valued positive) data. Three cases have been considered<OL><LI>Mixture of Poisson distributions.</LI><LI>Hidden Markov models with Poisson state conditional distribution.</LI><LI>Hidden Markov models with Negative binomial state conditional distribution.</LI></OL>Compared to the main <TT>H2M</TT> functions, only the case of ergodic models (ie.models that can be trained from a single long observation sequence rather thanfrom multiple sequences) has been considered.<P><BR><HR><ADDRESS>Olivier Cappé, Aug 24 2001</ADDRESS></BODY></HTML>
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