model {
theta ~ dbeta(a[pick], b[pick])
pick ~ dcat(q[1:2])
q[1] <- 0.50
q[2] <- 0.50
q.post[1] <- equals(pick, 1) # = 1 if prior 1 picked
q.post[2] <- equals(pick, 2) # = 1 if prior 2 picked
r ~ dbin(theta, n) # sampling distribution
r.pred ~ dbin(theta, m) # predictive distribution
}
Data:
list(r = 2, n = 5, m = 10, a = c(3,3), b = c(27,3))
node mean sd MC error 2.5% median 97.5% start sample
q.post[1] 0.2416 0.4281 0.004438 0.0 0.0 1.0 1001 50000
q.post[2] 0.7584 0.4281 0.004438 0.0 1.0 1.0 1001 50000
r.pred 3.789 2.328 0.0177 0.0 4.0 8.0 1001 50000
theta 0.3786 0.1843 0.00164 0.07455 0.3872 0.721 1001 50000