model{
   # Likelihood
   for(i in 1:m1){
      k1[i]~dbin(theta1,N)
   }
   for(i in 1:m2){
      k2[i]~dbin(theta2,N)
   }
   # Prior
   theta1~dbeta(1,1)
   theta2~dbeta(1,1)
   Deltheta<-theta1-theta2
   # Posterior predictive
   postk1~dbin(theta1,N)
   postk2~dbin(theta2,N)
}