In Bayesian analysis, the Markov Chain Monte Carlo (MCMC)
algorithm is an efficient and simple method to compute posteriors. However, the
chain may appear to converge while the posterior is improper, which will leads
to incorrect statistical inferences. In this paper, we focus on the necessary and
sufficient conditions for which improper hierarchical priors can yield proper
posteriors in a multivariate linear model. In addition, we carry out a simulation
study to illustrate the theoretical results, in which the Gibbs sampling and
Metropolis-Hasting sampling are employed to generate the posteriors.