📝 SummarXiv

Abstract

Gaussian random field is an ubiquitous model for spatial phenomena in diverse scientific disciplines. Its approximation is often crucial for computational feasibility in simulation, inference, and uncertainty quantification. The Karhunen-Lo\`eve Expansion provides a theoretically optimal basis for representing a Gaussian random field as a sum of deterministic orthonormal functions weighted by uncorrelated random variables. While this is a well-established method for dimension reduction and approximation of (spatial) stochastic process, its practical application depends on the explicit or implicit definition of the covariance structure. In this work we propose a novel approach to approximating Gaussian random field by explicitly constructing its covariance function from a regularized thin plate splines kernel. In a numerical analysis, the regularized thin plate splines kernel model, under a Bayesian approach, correctly capture the spatial correlation in the different proposed scenarios. Furthermore, the penalty term effectively shrinks most basis function coefficients toward zero, the eigenvalues decay and cumulative variance show that the proposed model efficiently reduces data dimensionality by capturing most of the variance with only a few basis functions. More importantly, from the numerical analysis we can suggest its strong potential for use beyond the Matern correlation function. In a real application, it behaves well when modeling the NO2 concentrations measured at monitoring stations throughout Germany. It has good predictive performance when assessed using the posterior medians and also demonstrate best predictive performance compared with another popular method to approximate a Gaussian random field.