Dear all,

I am trying to implement a JAGS model that takes as a prior a Mixture Model made out of 5 Gaussian distributions. These distributions differ only in term of their means.

Now, I'd like to sample with equal probability from everyone of these but, for some reason, it seems that the algorithm is not taking into account the probabilities that I am providing to the dcat function. (This code behaves ignoring the probabilities from 2 to 5 and samples ONLY from the mean.m[1])

Any help would be very much appreciated

Giovanni

Here is an example of what I have written:

# 5 different means mean.m[ 1 ] <- 0.000000 + 1.0E-12 mean.m[ 2 ] <- 1.570796 mean.m[ 3 ] <- 3.141593 mean.m[ 4 ] <- 4.712389 mean.m[ 5 ] <- 6.283185 - 1.0E-12 # common precision precis.m ~ dunif( 40 , 50 ) # equal prior probability pr.m[ 1 ] <- 0.2 pr.m[ 2 ] <- 0.2 pr.m[ 3 ] <- 0.2 pr.m[ 4 ] <- 0.2 pr.m[ 5 ] <- 0.2 # Mixture Model M ~ dcat( pr.m[] ) D <- mean.m[ M ] tau ~ dnorm( D , precis.m )T( 0 , 6.283185 )