The routines use a common data structure 'hmm' with fields:hmm.obsmodel		name of observation model			'Gauss'		- Gaussian			'GaussCom'	- Gaussian with common cov			'LIKE'		- Likelihood data, i.e. no obsmodel			'Poisson'	- Poissonhmm.train.obsupdate	update observation model (1 or 0)	 .pupdate	update transition matrix (1 or 0)	 .init		initialised (1 or 0)	 .cyc		max number of cycles through data	 .tol		termination tolerance of likelihoodhmm.data.Xtrain		training data	.T		length of training sequence			.Xtest		testing datahmm.K			number of hidden states   .Pi 			initial state probability   .P			state transition probabilities   .LPtrain		training log likelihoodhmm.gmmll		loglikelihood of gmm model used for initialisationhmm.mix			gaussian mixture model trained on same datahmm.priors.Dir2d_alpha   2-D Dirichlet prior counts for Tx Probabilities          .Dir_alpha     Dirichlet prior counts for hidden Probabilities @t=0For 'Gauss' and 'GaussCom' observation models we also have:hmm.state(k).Mu		mean vector for state k            .Cov	mean covariance matrix for state k	    .priors     priors for each state                   .norm_Mu     Prior for mean: mean (1,dimension(data))                   .norm_Cov    Prior for mean: covariance                   .norm_Prec   Prior for mean: precision                   .Wish_alpha  Prior for Covariance: scale parameter                   .Wish_B      Prior for Covariance: shape matrix                   .Wish_k      Prior for Covariance: dimension of shape matrixFor 'Poisson' observation models we also have:hmm.state(k).lambda	mean/rate of poisson pdf for state k	    .priors     priors for each state                   .norm_alpha  Prior for mean: scale                   .norm_beta    Prior for mean: shapeFor 'LIKE' observation models we have no parameters