model
	{
  
	#  PRIORS
	  bias[1]<-0
	  for (k in 2 : K) { bias[k] ~ dnorm(0,0.00001)} # vague priors
 	 # Loop around elections:
 	 rho ~ dexp(2)
	  
	 # LIKELIHOOD	
	  for (i in 1 : I) {     # loop around elections
	
     	# Multinomial model
         X[i,1:K] ~ dmulti( p[i,1:K] , n[i])
         n[i] <- sum(X[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]) 

     }
    }  
  }
}  
	
Data
list( I   = 12,   K   = 3,
	X = structure(.Data = c(
300,	315,	12,
325,	295,	9,
345,	277,	8,
365,	258,	7,
304,	317,	9,
253,	364,	13,
330,	288,	12,
297,	301,	37,
277,	319,	39,
339,	269,	27,
397,	209,	45,
376,	229,	45
), .Dim = c(12, 3)),
V = structure(.Data = c(
.4341994,	.4610934,	.1047072,
.4797144,	.4877813,	.0325043,
.4974225,	.4635792,	.0389983,
.4935235,	.4384425,	.0680339,
.4339797,	.4413256,	.1246946,
.4187992,	.4803503,	.1008505,
.4637579,	.430723,	.1055191,
.3788168,	.3715876,	.2495956,
.3584408,	.3925122,	.249047,
.4387356,	.3693695,	.1918949,
.4242528,	.2757294,	.3000178,
.423,	.308,	.269), .Dim = c(12, 3))
)
								
							
Inits

list(bias = c(NA, 0, 0),
	    rho =  0)