Abstract

Random field regression (RFR) models, in which a response is treated as the realization of a random field, have been advocated for modeling data from experiments in high signal-to-noise settings. In particular, RFR models have proven useful in analyzing data generated from computer simulations of complex processes. They offer flexibility for smoothing these data and are able to interpolate the known values for factor settings tasted on the simulator. However, these models lack the easy interpretability of standard regression stimulators. Our purpose in this chapter is to demonstrate that there is actually much common ground between RFR models and Bayesian regression and to provide some simple data analytics tools that can help expose a regression model associated will an RFR model.