DMOEM: The DMOEM is an overrelaxation algorithm in distributed...

In DOEM: The Distributed Online Expectation Maximization Algorithms to Solve Parameters of Poisson Mixture Models

The DMOEM is an overrelaxation algorithm in distributed manner, which is used to solve the parameter estimation of Poisson mixture model.

Description

The DMOEM is an overrelaxation algorithm in distributed manner, which is used to solve the parameter estimation of Poisson mixture model.

Usage

DMOEM(
  y,
  M,
  K,
  seed,
  alpha0,
  lambda0,
  MOEMalpha0,
  MOEMlambda0,
  omega,
  T,
  epsilon
)

Arguments

y	is a data matrix
M	is the number of subsets
K	is the number of Poisson distribution
seed	is the recommended way to specify seeds
alpha0	is the initial value of the mixing weight under the EM algorithm
lambda0	is the initial value of the mean under the EM algorithm
MOEMalpha0	is the initial value of the mixing weight under the monotonically overrelaxed EM algorithm
MOEMlambda0	is the initial value of the mean under the monotonically overrelaxed EM algorithm
omega	is the overrelaxation factor
T	is the number of iterations
epsilon	is the threshold value

Value

DMOEMtime,DMOEMalpha,DMOEMlambda

Examples

library(stats)
set.seed(637351)
K=5 
alpha1=c(rep(1/K,K)) 
lambda1=c(1,2,3,4,5) 
n=300 
U=sample(c(1:n),n,replace=FALSE)
y= c(rep(0,n)) 
for(i in 1:n){
if(U[i]<=0.2*n){
y[i] = rpois(1,lambda1[1])} 
else if(U[i]>0.2*n & U[i]<=0.4*n){
y[i] = rpois(1,lambda1[2])} 
else if(U[i]>0.4*n & U[i]<=0.6*n){
y[i] = rpois(1,lambda1[3])} 
else if(U[i]>0.6*n & U[i]<=0.8*n){
y[i] = rpois(1,lambda1[4])}
else if(U[i]>0.8*n ){
y[i] = rpois(1,lambda1[5])} 
}
M=5
seed=637351
set.seed(123) 
e=sample(c(1:n),K)
alpha0= MOEMalpha0=e/sum(e)
lambda0= MOEMlambda0=c(1.5,2.5,3.5,4.5,5.5)
omega=0.8
T=10
epsilon=0.005
DMOEM(y,M,K,seed,alpha0,lambda0,MOEMalpha0,MOEMlambda0,omega,T,epsilon)

DOEM documentation built on March 30, 2022, 9:06 a.m.