model
{
for (i in 1:m)
{
	# Poisson likelihood for observed counts
	y[i]~dpois(mu[i])
	mu[i]<-e[i]*theta[i]
	# Relative Risk
	theta[i]~dgamma(a,b)
	r[i]<-y[i]-mu[i]
}

# Prior distributions for "population" parameters
a~dexp(0.1)
b~dexp(0.1)

# Population mean and population variance
mean<-a/b
var<-a/pow(b,2)
}
list(m=46,
y=c(
46, 268, 26, 322, 43, 48, 186, 195, 26, 590, 
100, 71, 95, 83, 82, 140, 78, 149, 
41, 49, 278, 128, 662, 167, 50, 394, 30, 110,
 105, 126, 56, 396, 28, 81, 76, 84, 
155, 210, 172, 520, 32, 523, 223, 84, 96, 275), 
e=c(
49.43786, 269.0482, 23.00089, 322.7169, 
33.11245, 43.68563, 218.6871, 274.0518, 
28.20118, 635.1979, 98.68706, 69.04482, 
82.44996, 61.8455, 74.99173, 133.2004, 
59.70397, 176.8881, 40.14719, 44.94607, 
250.6896, 107.8332, 710.1876, 127.6951, 
38.53552, 350.7576, 34.10996, 97.52899,
 118.1897, 126.9444, 40.94199, 411.9688, 
19.15737, 69.46429, 59.38685, 69.16725, 
128.5702, 176.3502, 214.9299, 616.2793, 
34.17017, 496.6627, 215.0102, 61.20525, 
74.50402, 309.7152))