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Activity Number: 474
Type: Contributed
Date/Time: Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #306538
Title: Calibration and Prediction for Computer Experiment Output Having Qualitative and Quantitative Input Variables
Author(s): Gang Han*+ and Thomas Santner and William Notz
Companies: The Ohio State University and The Ohio State University and The Ohio State University
Address: 577 Harley Drive, Apt. 7, Columbus, OH, 43202,
Keywords: calibration ; Gaussian stochastic processes ; power exponential correlation ; kriging
Abstract:

We propose statistical models for prediction and calibration that allow both qualitative and quantitative input variables. The model allow prediction of a computer code at an untested set of qualitative and quantitative inputs as well as quantifying the uncertainty in the prediction. In the case of calibration, both the physical experiment and computer code are allowed to depend on both types of variables. A Bayesian Qualitative and Quantitative Variable (QQV) model is constructed and implemented by Markov Chain Monte Carlo methodology. This model is compared with a frequentist approach and a Bayesian independence model in several examples.

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