Variable Selection for Gaussian Process Models in Computer Experiments
Authors:
- Crystal LinkletterCLCrystal Linkletter
- Derek BinghamDBDerek Bingham
- Nicholas HengartnerNHNicholas Hengartner
- David HigdonDHDavid Higdon
- Kenny Q YeKYKenny Q Ye
- 1 November 2006
- journal article
- Published by Taylor & Francis in Technometrics
- Vol. 48 (4) , 478-490
Abstract
In many situations, simulation of complex phenomena requires a large number of inputs and is computationally expensive. Identifying the inputs that most impact the system so that these factors can be further investigated can be a critical step in the scientific endeavor. In computer experiments, it is common to use a Gaussian spatial process to model the output of the simulator. In this article we introduce a new, simple method for identifying active factors in computer screening experiments. The approach is Bayesian and only requires the generation of a new inert variable in the analysis; however, in the spirit of frequentist hypothesis testing, the posterior distribution of the inert factor is used as a reference distribution against which the importance of the experimental factors can be assessed. The methodology is demonstrated on an application in material science, a computer experiment from the literature, and simulated examples.Keywords
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