model
   {
      for( i in 1 : N )
      {
            x[i] ~ dlog.logis(beta, theta)
      }
      
   # Prior distributions of the model parameters    
   
         beta ~ dunif(0.1, 10.0)
         theta~ dunif(0.1, 10.0)      
   }