“…Rather, the method by which this bias is quantified is inconsequential to the way in which bias-corrected partitioned analysis of said bias is applied to the prediction, and as such the method selected for computer experiments. What is important, however, is the accuracy with which the method for quantifying bias is able to train the discrepancy function (Stevens and Atamturktur, 2015) as well as assessing the calibration of parameters and inference of bias in a connected manner (Farajpour and Atamturktur, 2014). A variety of methods are available for inferring bias in the constituents, starting with regression-based approaches directly relating bias to tested control settings, be they as simple linear functions (Derber and Wu, 1998), high degree polynomials where coefficients are determined stochastically (Steinberg, 1985), up to non-parametric fits such as a Gaussian process model (Sacks et al, 1989;Kennedy and O'Hagan, 2001;Bayarri et al, 2007), and continuing away to methods for determining relationships between discrepancy and control settings such as a maximum likelihood estimation of parameter distribution characteristics (Xiong et al, 2009;Atamturktur et al, 2015b) or a copula-based approach (Xi et al, 2014).…”