چكيده لاتين :
This paper deals with Bayesian geostatistical prediction under the Matern correlation function
which involves a smoothness parameter in addition to the range parameter. In fact, we consider the reference
prior for the range parameter and an inverse gamma prior for the smoothness parameter. We then propose an
attractive and straightforward Monte Carlo method to sample the posterior distribution of the model
parameters and achieve Bayesian prediction. In a sensitivity analysis, the importance of the prior choice is
assessed. Since the posterior results greatly depend on the prior hyperparameters, the Monte Carlo EM
algorithm is applied to determine their maximum likelihood estimates. Finally, we utilize this procedure in the
geostatistical prediction of carbon monoxide concentrations in Tehran.
