model {
for (i in 1:N) {
y[i] ~ dpois(m[i])
m[i] <- group[i]*mu # mean is 0 if group is 0
group[i] ~ dbern(p)
}
# proportion of claims that could be positive
p ~ dunif(0,1)
mu ~ dgamma(0.5, 0.0001) # approximate Jeffreys prior
}
Data:
list(N=35,
y=c(0,0,0,0,0,0,0,0,0,0,
1,1,1,1,1,2,2,2,2,2,
2,2,2,2,2,3,3,3,3,3,
3,3,4,4,5))
node mean sd MC error 2.5% median 97.5% start sample
mu 2.066 0.3145 0.004767 1.497 2.047 2.733 1001 10000
p 0.8102 0.08947 0.001798 0.6252 0.8143 0.9701 1001 10000