EM for HMM Multivariate Gaussian processes 1.0

em_ghmm : Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement Usage ------- [logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options]); Inputs ------- Z Measurements (m x K x n1 x ... x nl) PI0 Initial probabilities (d x 1) : Pr(x_1 = i) , i=1,...,d. PI0 can be (d x 1 x v1 x ... x vr) A0 Initial state transition probabilities matrix Pr(x_{k} = i| x_{k - 1} = j) such sum_{x_k}(A0) = 1 => sum(A , 1) = 1. A0 can be (d x d x v1 x ... x vr). M0 Initial mean vector. M0 can be (m x 1 x d x v1 x ... x vr) S0 Initial covariance matrix. S0 can be (m x m x d x v1 x ... x vr) options nb_ite Number of iteration (default [30]) update_PI Update PI (0/1 = no/[yes]) update_A Update PI (0/1 = no/[yes]) update_M Update M (0/1 = no/[yes]) update_S Update S (0/1 = no/[yes]) Outputs ------- logl Final loglikelihood (n1 x ... x nl x v1 x ... x vr) PI Estimated initial probabilities (d x 1 x n1 x ... x nl v1 x ... x vr) A Estimated state transition probabilities matrix (d x d x n1 x ... x nl v1 x ... x vr) M Estimated mean vector (m x 1 x d x n1 x ... x nl v1 x ... x vr) S Estimated covariance vector (m x m x d x n1 x ... x nl v1 x ... x vr)Please run mexme_em_ghmm to compile mex files on your platform.Run test_em_ghmm for demo

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Default,  Iteration,  Noyes,  Number,  Outputs,  Update
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