HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999Matlab toolbox for Hidden Markov Models(Adapted from Machine Learning ToolboxVersion 1.0  01-Apr-96Copyright (c) by Zoubin Ghahramani, University of Toronto)The software uses some NETLAB routines (see http://neural-server.aston.ac.uk/netlab/index.html)so you'll need to have NETLAB on your search pathSee the file VERSION for what's new in this version.The following observation models have so far been implemented:Gaussian, Gaussian with common covariances, Likelihood, AR.DEMONSTRATIONS:demgausshmm.m       uses Gaussian observation model on AR featuresdemlike.m        the time series values are themselves likelihoodsdemar.m          uses AR observation model on original time seriesROUTINES:hmminit          initialise Gaussian observation HMMhmmtrain         train HMMhmmdecode        make classifications using HMMobsinit          initialise Gaussian observation modelobslike          calculate likelihood of data given observation modelobsupdate        update parameters of observation modelrsum             row sum of matrixrprod            row product of matrix and vectorrdiv             row division of matrix by vectorTo extend the HMM to different observation models add the requiredcode into obsinit, obslike and obsupdate.DATA:Read the file DATA-STRUCTURE.