Approximating Likelihoods for Large Spatial Data Sets
大型空间数据集的似然近似
Summary Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for n observations require O(n3) operations. Since any joint density can be written as a product of conditional densities based on some ordering of the observations, one way to lessen the computations is to condition on only some of the ‘past’ observations when computing the…