
	model {
		# prior distributions
		psi ~ dunif(0, 1)
		p ~ dunif(0,1)
		# zero-inflated binomial model for the augmented data
		for(i in 1 : nind + nz){
			z[i] ~ dbern(psi)
			mu[i] <- z[i ]* p
			y[i] ~ dbin(mu[i], J)
		}
		# N is a derived parameter under data augmentation
		N<-sum(z[])
	}
