model{
  #Likelihood
  for(i in 1:N){
      Y[i]~dnorm(mu[i],tau)
      mu[i]<-intercept + inprod(x[i,],beta[])
  }
  tau~dgamma(0.1,0.1)
  intercept~dnorm(0,0.01)
  for(j in 1:p){beta[j]~dnorm(0,0.01)}

  #Model for x
  for(i in 1:N){x[i,1:p]~dmnorm(mn.x[],prec.x[,])}
  for(j in 1:p){mn.x[j]~dnorm(0,0.01)}
  prec.x[1:p,1:p]~dwish(R[,],k)
  cov.x[1:p,1:p]<-inverse(prec.x[,])
  k<-p+0.1
  for(j1 in 1:p){for(j2 in 1:p){R[j1,j2]<-0.1*equals(j1,j2)}} #R is diagonal
}

#Stacks Data
list(p = 3,   N = 21,  
        #The missing Y was 37
	Y = c(42, 37, NA, 28, 18, 18, 19, 20, 15, 14, 14, 13, 11, 12, 8, 7, 8, 8, 9, 15, 15),
	x = structure(.Data = c( 80, 27, 89,
  				 80, 27, 88,
				 75, 25, 90,
				 62, NA, 87, #The missing X was 24 (Y=28)
				 62, NA, 87, #The missing X was 22 (Y=18)
				 62, 23, 87,
				 62, 24, 93,
				 62, 24, 93,
				 58, 23, 87,
				 58, 18, 80,        
				 58, 18, 89,
				 58, 17, 88,
				 58, 18, 82,
				 58, 19, 93,
				 50, 18, 89,
				 50, 18, 86,
				 50, NA, 72, #The missing X was 19 (Y=8)
				 50, NA, 79, #The missing X was 19 (Y=8)
				 50, 20, 80,
				 56, 20, 82,
				 70, 20, 91), .Dim = c(21, 3)))