Authors: Dette, Holger
Pepelyshev, Andrey
Title: NPUA: A new approach for the analysis of computer experiments
Language (ISO): en
Abstract: The main issue in the analysis of computer experiments is an uncertainty of prediction and related inferences. To address the uncertainty analysis, the Bayesian analysis of deterministic computer models has been actively developed in the last decade. In the Bayesian approach, the uncertainty is expressed through a Gaussian process model. As a consequence, the resulting analysis is rather sensitive with respect to these prior assumptions. Moreover, for high dimensional data this approach leads to time consuming computations. In the present paper we introduce a new approach for deriving the uncertainty in the analysis of computer experiments, where the distribution of uncertainty is obtained in a general nonparametric form. The proposed approach is called N(on) P(arametric) U(ncertainty) A(nalysis) and is based on a combination of sampling and regression techniques. In particular, it is computationally very simple. We compare NPUA with the Bayesian and Kriging method and investigate its performance for finding points for the next runs by re-analyzing the ASET model.
Subject Headings: Computer experiment
Important sampling
Jack-knife
Regression
Sequential design
Uncertainty analysis
URI: http://hdl.handle.net/2003/26621
http://dx.doi.org/10.17877/DE290R-8817
Issue Date: 2010-01-18T10:55:05Z
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_0110_SFB823_dette_pepelyshev.pdfDNB445.75 kBAdobe PDFView/Open


This item is protected by original copyright



This item is protected by original copyright rightsstatements.org