mixdiagmvnorm_mstep function

the M step function of the EM algorithm

The M step function of the EM algorithm for the mixture of multivariate normals with diagonal covariance matrix as the emission distribution using the observation matrix and the estimated weight vectors

mixdiagmvnorm_mstep(x, wt1, wt2)

Arguments

• x: the observation matrix
• wt1: the state probabilities matrix (number of observations times number of states)
• wt2: the mixture components probabilities list (of length nstate) of matrices (number of observations times number of mixture components)

Returns

list of emission (mixture multivariate normal) parameters: (mu, sigma and mix.p), where sigma is a diagonal matrix

Examples

data(CMAPSS)
n = nrow(CMAPSS$train$x)
wt1 <- matrix(runif(3 * n), nrow = n, ncol = 3)
wt2 <- list()
for(j in 1:3) wt2[[j]] <- matrix(runif(5 * n), nrow = n, ncol = 5)
emission <- mixdiagmvnorm_mstep(CMAPSS$train$x, wt1, wt2)

Author(s)

Morteza Amini, morteza.amini@ut.ac.ir