em_ghmm : ExpectationMaximization 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
