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)               
   }