model
   {
      for( i in 1 : N )
      {
      x[i] ~ dinv.gauss(mu, lambda)
            
      }
      
   # Prior distributions of the model parameters   
   
         mu ~ dunif(0.001, 10.0)
         lambda~ dunif(0.01, 5.0)      
   }