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Abstract Details
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Activity Number:
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638
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Type:
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Invited
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #303875 |
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Title:
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Kernel Approximation: From Regression to Interpolation
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Author(s):
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Roshan Joseph Vengazhiyil*+ and Lulu Kang
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Companies:
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Georgia Institute of Technology and Illinois Institute of Technology
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Address:
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School of Industrial and Systems Engineering, Atlanta, GA, 30332,
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Keywords:
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Computer Experiments ;
Gaussian Process ;
Kriging
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Abstract:
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We propose a new interpolation method known as kernel interpolation (KI) for modeling the output from expensive deterministic computer experiments. It is obtained by repeating the classical Nadaraya-Watson kernel regression infinite number of times. We also provide a probabilistic framework for KI using a nonstationary Gaussian process model, which enables us to quantify the uncertainty in the predictions as well as estimate the unknown parameters in the model using empirical Bayes methods. The performance of KI is compared with the popular kriging method using several simulations and real applications.
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