EM: The EM algorithm is used to solve the parameter estimation of...

In DEM: The Distributed EM Algorithms in Multivariate Gaussian Mixture Models

The EM algorithm is used to solve the parameter estimation of multivariate Gaussian mixture model.

Description

The EM algorithm is used to solve the parameter estimation of multivariate Gaussian mixture model.

Usage

EM(y, alpha0, mu0, sigma0, i, epsilon)

Arguments

y	is a data matrix
alpha0	is the initial value of the mixing weight
mu0	is the initial value of the mean
sigma0	is the initial value of the covariance
i	is the number of iterations
epsilon	is the threshold value

Value

EMalpha,EMmu,EMsigma,EMtime

Examples

library(mvtnorm)
alpha1= c(rep(1/4,4)) 
mu1=matrix(0,nrow=4,ncol=4) 
for (k in 1:4){
mu1[4,]=c(runif(4,(k-1)*3,k*3)) 
}
sigma1=list()
for (k in 1:4){
sigma1[[k]]= diag(4)*0.1
}
y= matrix(0,nrow=200,ncol=4) 
for(k in 1:4){
y[c(((k-1)*200/4+1):(k*200/4)),] = rmvnorm(200/4,mu1[k,],sigma1[[k]]) 
}
alpha0= alpha1
mu0=mu1
sigma0=sigma1
i=10
epsilon=0.005
EM(y,alpha0,mu0,sigma0,i,epsilon)

DEM documentation built on May 14, 2022, 9:05 a.m.