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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #304133 |
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Title:
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Fast Calibration of Complex Computer Models
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Author(s):
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Matthew T. Pratola*+ and Stephan R. Sain and Derek Bingham
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Companies:
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Simon Fraser University and National Center for Atmospheric Research and Simon Fraser University
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Address:
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8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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Keywords:
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Calibration Parameter Estimation ; Gaussian Process ; Sequential Design ; Kullback-Liebler ; Magnetosphere
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Abstract:
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Computer models enable scientists to investigate real-world phenomena in a virtual laboratory using computer experiments. Recently, statistical calibration enabled scientists to incorporate field data. However, the practical application is hardly straightforward. For instance, large and non-stationary computer model output is not well addressed. We present a computationally efficient approach using a criterion that measures discrepancy between the computer model and field data. One can then construct empirical distributions for the parameters and perform sequential design. The strength of this approach is its simple computation using existing algorithms. Our method also provides good parameter estimates for large and non-stationary data.
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