model {
p[1:numPrey] ~ ddirch(alpha[]); # these are weights for means
for(i in 1:numPrey) {
p2[i] <- p[i]*p[i]; # these are weights for variances
}
# for each isotope, calculate the predicted mixtures
for(i in 1:numIsotopes) {
mix.mu[i] <- inprod(u[,i],p[]);
mix.var[i] <- inprod(vars[,i],p2[]);
mix.totalVar[i] <- mix.var[i];
mix.prcsn[i] <- 1/(mix.totalVar[i]);
}
# This section does the likelihood / posterior, looping over N data points
for(i in 1:N) {
for(j in 1:numIsotopes) {
X[i,j] ~ dnorm(mix.mu[j], mix.prcsn[j]);
}
}
}