RobustGaSP: Robust Gaussian Stochastic Process Emulation in R

01/05/2018 ∙ by Mengyang Gu, et al. ∙ 0

Gaussian stochastic process (GaSP) emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a challenging task. No closed-form estimator is available and many numerical problems arise with standard estimates, e.g., the maximum likelihood estimator (MLE). In this package, we implement a marginal posterior mode estimator, for special priors and parameterizations, an estimation method that meets the robust parameter estimation criteria discussed in Gu2016thesis,Gu2016robustness; mathematical reasons are provided therein to explain why robust parameter estimation can greatly improve predictive performance of the emulator. The package also allows inert inputs (inputs that almost have no effect on the variability of a function) to be identified from the marginal posterior mode estimation, at no extra computational cost. The package can be operated in a default mode, but also allows numerous user specifications, such as the capability of specifying trend functions and noise terms. Examples are studied herein to highlight the performance of the package in terms of out-of-sample prediction.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.