Spatial phenomena is commonly modelled as a realization from a stochastic process. Even when the reality is unique such models can usefully represent the uncertainty the modeler has about the phenomena. This paper is concerned with predicting for …
This article is concerned with predicting for Gaussian random fields in a way that appropriately deals with uncertainty in the covariance function. To this end, we analyze the best linear unbiased prediction procedure within a Bayesian framework. …
This thesis is concerned with aspects of statistical inference for Gaussian random fields when the ultimate objective is prediction. If we wish to predict a value for a random field at unobserved locations in a bounded region it is essential to have …