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SUMMARY:Coupled Gaussian Process Models - Joseph\, R V (Georgia Institute 
 of Technology)
DTSTART:20110908T110000Z
DTEND:20110908T113000Z
UID:TALK32719@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Gaussian Process (GP) models are commonly employed in computer
  experiments for modeling deterministic functions. The model assumes secon
 d-order stationarity and therefore\, the predictions can become poor when 
 such assumptions are violated. In this work\, we propose a more accurate a
 pproach by coupling two GP models together that incorporates both the non-
 stationarity in mean and variance. It gives better predictions when the ex
 perimental design is sparse and can also improve the prediction intervals 
 by quantifying the change of local variability associated with the respons
 e. Advantages of the new predictor are demonstrated using several examples
  from the literature.\n
LOCATION:Seminar Room 1\, Newton Institute
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