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