Bayesian Spatial Prediction
Dr. Benjamin Kedem
Department of Statistics
University of Maryland, College Park
Abstract
We discuss Bayesian spatial/temporal prediction in transformed Gaussian
random fields where the transformation belongs to a parametric family.
Monte Carlo integration is used in the approximation of the predictive
density function, which is easy to implement in this framework. The BTG
software for the implementation of the method will be discussed by
means of spatial and temporal examples. As a byproduct, we provide
a Bayesian way to tackle the distribution problem of average rainfall
rate.