model {
r <- 1; n <- 2; a[1] <- 1; a[2] <- 5; a[3] <- 20
for (i in 1:3) {
a.inv[i] <- 1/a[i]
theta[i] <- pow(psi[i], a.inv[i])
psi[i] ~ dbeta(a.inv[i], 1)
}
r1 <- r; r2 <- r; r3 <- r # replicate data
r1 ~ dbin(theta[1], n)
r2 ~ dbin(theta[2], n)
r3 ~ dbin(theta[3], n)
}

Inits:
list(psi=c(0.5,0.2,0.05))

Dbar = post.mean of -2logL; Dhat = -2LogL at post.mean of stochastic nodes
   Dbar   Dhat   pD   DIC   
r1   1.945   1.386   0.559   2.505   
r2   1.945   1.550   0.395   2.341   
r3   1.933   2.289   -0.356   1.577   
total   5.824   5.225   0.599   6.422   

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   psi[1]   0.5005   0.2234   3.174E-4   0.09535   0.5005   0.9056   1001   500000
   psi[2]   0.1075   0.1642   5.941E-4   7.481E-6   0.03163   0.6081   1001   500000
   psi[3]   0.01192   0.05659   3.621E-4   1.122E-20   1.187E-6   0.1385   1001   500000
   theta[1]   0.5005   0.2234   3.174E-4   0.09535   0.5005   0.9056   1001   500000
   theta[2]   0.5009   0.2235   0.001167   0.09436   0.5012   0.9053   1001   500000
   theta[3]   0.5046   0.2213   0.004354   0.1006   0.5055   0.9059   1001   500000

[example-8_6_2-transformed-binomial0][example-8_6_2-transformed-binomial1][example-8_6_2-transformed-binomial2][example-8_6_2-transformed-binomial3][example-8_6_2-transformed-binomial4][example-8_6_2-transformed-binomial5]