	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)		
	}