model
	{
  
	#  PRIORS
	  bias[1]<-0
	  rho ~ dnorm(mu.rho,0.0001)
          log(mu.rho)<-0

       for (k in 2:K) {bias[k] ~ dnorm(0,0.0001)} # vague priors

 	 	  
	 # LIKELIHOOD	
	  for (i in 1 : N) {     # loop around elections
     	# Multinomial model
         S[i,1:K] ~ dmulti(p[i,1:K],n[i])
         n[i] <- sum(S[i,])
         for (k in 1:K) {     # loop around parties
            p[i,k]        <-  phi[i,k] / sum(phi[i,])
            log(phi[i,k]) <- bias[k] + rho*log(V[i,k])
                        
      }
    }  
  }
	
}