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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Interpolation of Deterministic Simulator Outputs u
sing a Gaussian Process Model - Ranjan\, P (Acadia
University)
DTSTART;TZID=Europe/London:20110909T090000
DTEND;TZID=Europe/London:20110909T093000
UID:TALK32736AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/32736
DESCRIPTION:For many expensive deterministic computer simulato
rs\, the outputs do not have replication error and
the desired metamodel (or statistical emulator) i
s an interpolator of the observed data. Realizatio
ns of Gaussian spatial processes (GP) are commonly
used to model such simulator outputs. Fitting a G
P model to n data points requires the computation
of the inverse and determinant of n x n correlatio
n matrices\, R\, that are sometimes computationall
y unstable due to near-singularity of R. This happ
ens if any pair of design points are very close to
gether in the input space. The popular approach to
overcome near-singularity is to introduce a small
nugget (or jitter) parameter in the model that is
estimated along with other model parameters. The
inclusion of a nugget in the model often causes un
necessary over-smoothing of the data. In this talk
\, we present a lower bound on the nugget that min
imizes the over-smoothing and an iterative regular
ization approach to construct a predictor th at fu
rther improves the interpolation accuracy. We also
show that the proposed predictor converges to the
GP interpolator.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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