mixdiagmvnorm_mstep {hhsmm} | R Documentation |
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, morteza.amini@ut.ac.ir
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)
[Package hhsmm version 0.4.0 Index]