Speaker
Description
Gaussian processes (GPs) defined through intrinsic random fields provide a flexible framework for modeling spatial phenomena, and have been advocated in a variety of applications over the past several decades. Nevertheless, their adoption has lagged behind traditional, covariance-based approaches, in part because the intrinsic formulation has lacked an accompanying toolkit of computational methods and dependence specifications that facilitate fitting and prediction. This work develops a systematic framework for modeling intrinsic GPs and introduces practical algorithms for working with dependence/variogram models for modeling, inference and computation that parallel those of traditional, stationary GPs, highlighting the advantages of intrinsic-field modeling in terms of robustness, interpretability, and computational efficiency.