model
  {
    # priors on the vector of multinormal means
    mu[1:V] ~ dmnorm(mu.prior[], mu.prior.prec[,])
    # priors on the covariance matrix
    prec[1:V,1:V] ~ dwish(covmat.prior[,], covmat.prior.DF)
    
    # makes covariance from precision
    cov[1:V,1:V] <- inverse(prec[,])

    # likelihood
    for (i in 1:N)
    {
      Y[i,1:V] ~ dmnorm(mu[], prec[,])
    }
  }