model
{
for( i in 1 : N )
{
x[i] ~ dexp.weib(alpha, theta)
}
# Prior distributions of the model parameters
#alpha ~ dunif(0.001, 5.0)
#theta~ dunif(0.01, 20.0)
alpha ~ dgamma(0.001, 0.001)
theta~ dgamma(0.001, 0.001)
}