Hi
I am trying to calculate the probability of a data vector belonging to at
certain group, k. The groups are defined by known vectors (pi[k,1:M]) of
probabilities of multinomial distributions:
##K=3, M=14,N=263,alfa=c(1,1,1),
##X[i,1:M] is i'th data vector containing M variables
##the pi[,] are know but their derivation is not shown here
for (i in 1:N){
X[i,1:M]~dmulti(pi[class[i],1:M],n[i])
class[i]~dcat(pk[1:K])
n[i]<-sum(X[i,1:M])
}
pk[1:K]~ddirch(alfa[1:K])
the idea is then to monitor the class-variable to derive the probabilities.
I find that this type of model, at least in my case, is mixing very poorly -
or not at all. I wondered if anyone else have had any experience with this
kind of modelling?
Kind regards
Carsten
Carsten Hvingel
Pinngortitaleriffik/Greenland Inst. of Natural Resources
Box 570, DK-3900 Nuuk
Phone +299 321095
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