
	model { 
	# prior distributions 
		psi~dunif(0,1) 
		mu~dnorm(0,0.001) 
		tau~dgamma(.001,.001) # zero-inflated binomial mixture model for 
		                                          # the augmented data 
		for(i in 1: nind + nz){ 
			z[i] ~ dbin(psi,1) 
			eta[i]~ dnorm(mu, tau) 
			logit(p[i])<- eta[i] 
			muy[i]<-p[i] * z[i] 
			y[i] ~ dbin(muy[i], J) 
		} 
		# Derived parameters 
		N<-sum(z[1 : nind+nz]) 
		sigma<-sqrt(1  /tau) 
	}   
