mixdiagmvnorm_mstep: the M step function of the EM algorithm

In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

the M step function of the EM algorithm

Description

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

Usage

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)

Value

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

Author(s)

Morteza Amini, [e-mail]

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)

hhsmm documentation built on Aug. 8, 2023, 9:06 a.m.