# EM for HMM Multivariate Gaussian processes (Scripts) 1.0

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## EM for HMM Multivariate Gaussian processes (Scripts) Publisher's description

### em_ghmm : Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement

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)

Run test_em_ghmm for demo

#### System Requirements:

MATLAB 7.5 (R2007b)
Program Release Status: New Release
Program Install Support: Install and Uninstall

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