model {
for (i in 1:9) {
lower[i] <- y[i] - 0.5
upper[i] <- y[i] + 0.5
z[i] ~ dnorm(mu, 1)I(lower[i], upper[i])
}
mu ~ dunif(0, 100)
}
Data:
list(y=c(6,6,6,7,7,7,8,8,8))
Inits:
list(mu=6.5)
node mean sd MC error 2.5% median 97.5% start sample
mu 7.001 0.3496 0.00364 6.322 7.003 7.692 1001 10000
z[1] 6.08 0.2778 0.002909 5.543 6.113 6.483 1001 10000
z[2] 6.08 0.2779 0.002875 5.545 6.116 6.482 1001 10000
z[3] 6.076 0.2781 0.002511 5.548 6.11 6.483 1001 10000
z[4] 6.993 0.2829 0.002573 6.527 6.991 7.474 1001 10000
z[5] 7.0 0.2834 0.002608 6.527 7.003 7.468 1001 10000
z[6] 6.999 0.2859 0.003123 6.525 7.001 7.473 1001 10000
z[7] 7.922 0.2759 0.002482 7.517 7.885 8.458 1001 10000
z[8] 7.918 0.2768 0.002489 7.517 7.882 8.454 1001 10000
z[9] 7.923 0.2794 0.002949 7.517 7.89 8.458 1001 10000