Hi,
I am trying to learn lognormal mixture models with EM.
I was wondering how does one compute the log likelihood.
The current implementation I have is as follows,
which perform really bad in learning the mixture models.
__BEGIN__
# compute probably density of lognormal.
dens <- function(lambda, theta, k){
temp<-NULL
meanl=theta[1:k]
sdl=theta[(k+1):(2*k)]
for(j in 1:k){
# each being lognormal distribution
temp=cbind(temp,dlnorm(x,meanlog=meanl[j],sdlog=sdl[j]))
}
temp=t(lambda*t(temp))
temp
}
old.obs.ll <- sum(log(apply(dens(lambda, theta, k),1,sum)))
# this is prior likelihood
lognorm.ll <- function(theta, z,lambda, k) -
sum(z*log(dens(lambda,theta,k)))
__END__
It is based on a slight modification of our earlier
Gamma version, which works really well. The full
code of Gamma version can be found here:
http://dpaste.com/65353/plain/
-G.V.
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