Approximating likelihoods for large spatial data sets

Given a set of spatial data, often the desire is to estimate its covariance structure. For prac-tical purposes, it is often necessary to propose some parametric model for the variagram. Once the parametric model is selected, one must estimate the parameters of the model. Given a parametric structure, it naturally follows that maximum likelihood or Bayesia