model
	{
	   for( i in 1 : nareas) {
	
	        # Poisson likelihood for observed counts
	         oss[i]~dpois(lambda[i])   

		lambda[i] <-theta[i,T[i]]*att[i]
	
	
# Poisson means by group
     theta[i,1] <- 1
     theta[i,2] ~ dgamma(alpha,beta) 

# group membership
  T[i]  ~ dcat(P[]);

#posterior prob of belonging to group  1 or 2 (method 1)
    Tr[i,1] <- equals(T[i],1)
    Tr[i,2] <- equals(T[i],2)

 rr[i]<-Tr[i,1]*theta[i,1]+Tr[i,2]*theta[i,2]

	         
	      }
	      # Prior distributions for "population" parameters
	      alpha ~ dexp(0.1)
              beta ~  dexp(0.1)
		#prior on mixture proportions 

	P[1:2]~ ddirch(gamma[])
          }