var
   x[T],mu[N,T],Y[N,T],beta[N,2],mu.beta[2],
   Sigma2.beta[2,2],sigma.beta[2],tau.c,sigma,R[2,2],r,alpha0,
   tau.beta[2];
model {
    for (i in 1:N) {
      for (j in 1:T) {
        Y[i,j] ~ dnorm(mu[i,j],tau.c);  #
        mu[i,j] <- beta[i,1] + beta[i,2]*(x[j]-mean(x[])); 
     }
     beta[i,1] ~ dnorm(mu.beta[1],tau.beta[1]);  # independent Normals
     beta[i,2] ~ dnorm(mu.beta[2],tau.beta[2]);  # independent Normals
     }
 
# intercept at zero for centred model
   alpha0 <- mu.beta[1] - mu.beta[2]* mean(x[]); 

# prior for sampling precision
  tau.c  ~ dgamma(1.0E-3,1.0E-3);  sigma <- 1.0/sqrt(tau.c);

# parameters considered independent
  for (k in 1:2){
   tau.beta[k]  ~ dgamma(1.0E-3,1.0E-3);
   sigma.beta[k]<-1/sqrt(tau.beta[k]);
  }
 
  mu.beta[1] ~ dnorm(0,.00001);   # `flat' univariate Normal prior on mean
  mu.beta[2] ~ dnorm(0,.00001);   # `flat' univariate Normal prior on mean
}