Trans-Gaussian Kriging in a Bayesian framework : a case study

TL;DR

This paper introduces a Bayesian approach to Trans-Gaussian Kriging, enhancing spatial data modeling for complex, non-Gaussian datasets, with an application in nuclear safety testing.

Contribution

It extends Bayesian Kriging to Trans-Gaussian frameworks, providing an elegant and efficient method for modeling non-Gaussian spatial data.

Findings

Effective modeling of non-Gaussian spatial data

Application to nuclear safety testing procedures

Demonstrated computational efficiency

Abstract

In the context of Gaussian Process Regression or Kriging, we propose a full-Bayesian solution to deal with hyperparameters of the covariance function. This solution can be extended to the Trans-Gaussian Kriging framework, which makes it possible to deal with spatial data sets that violate assumptions required for Kriging. It is shown to be both elegant and efficient. We propose an application to computer experiments in the field of nuclear safety, where it is necessary to model non-destructive testing procedures based on eddy currents to detect possible wear in steam generator tubes.