model {
y.obs <- 4
df[1] <-1; df[2] <- 2; df[3] <- 4
df[4] <- 10; df[5] <- 50; df[6] <- 1000
for (i in 1:6) {
y[i] <- y.obs # replicate data
y[i] ~ dnorm(mu[i], 1)
mu[i] ~ dnorm(0, lambda[i])
lambda[i] <- X[i]/df[i] # precision is chi-square/df
X[i] ~ dchisqr(df[i])
# compare with prior distributions
mu.rep[i] ~ dnorm(0, lambda.rep[i])
lambda.rep[i] <- X.rep[i]/df[i]
X.rep[i] ~ dchisqr(df[i])
}
}

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   lambda[1]   0.2091   0.3384   0.004393   0.00338   0.1054   1.072   1001   10000
   lambda[2]   0.3047   0.3576   0.004708   0.0155   0.1954   1.252   1001   10000
   lambda[3]   0.4709   0.3992   0.005804   0.05558   0.3619   1.536   1001   10000
   lambda[4]   0.6988   0.3487   0.004382   0.2132   0.6339   1.56   1001   10000
   lambda[5]   0.9299   0.1895   0.001948   0.5993   0.9165   1.333   1001   10000
   lambda[6]   0.9961   0.045   3.477E-4   0.9095   0.9951   1.086   1001   10000
   mu[1]   3.449   1.072   0.01359   1.321   3.456   5.482   1001   10000
   mu[2]   3.212   1.065   0.01376   1.135   3.198   5.316   1001   10000
   mu[3]   2.859   1.035   0.01576   0.9113   2.833   4.97   1001   10000
   mu[4]   2.444   0.8902   0.01013   0.7911   2.42   4.277   1001   10000
   mu[5]   2.084   0.7478   0.007878   0.6675   2.071   3.575   1001   10000
   mu[6]   2.004   0.7031   0.00638   0.6264   1.996   3.401   1001   10000