Pritam Ranjan
Gaussian Process Models and Interpolators for Deterministic Computer Simulators.
Mon, 03/08/2010 - 16:01 — SomNambulAuthors: Pritam Ranjan, Ronald Haynes, Richard Karsten
Subjects: Methodology
AbstractFor many expensive deterministic computer simulators, the outputs do not have
replication error and the desired metamodel (or emulator) is an interpolator of
the observed data. Realizations of Gaussian spatial processes (GP) are commonly
used to model such simulator outputs. Fitting a GP model to $n$ data points
requires inversion of $n \times n$ correlation matrices, $R$, that are
sometimes computationally unstable due to near-singularity of $R$. This happens
if any pair of design points are very close together in the input space.Branch and Bound Algorithms for Maximizing Expected Improvement Functions.
Thu, 03/04/2010 - 16:01 — SomNambulAuthors: Pritam Ranjan, Mark Franey, Hugh Chipman
Subjects: Methodology
AbstractDeterministic computer simulations are often used as a replacement for
complex physical experiments. Although less expensive than physical
experimentation, computer codes can still be time-consuming to run. An
effective strategy for exploring the response surface of the deterministic
simulator is the use of an approximation to the computer code, such as a
Gaussian process (GP) model, coupled with a sequential sampling strategy for
choosing design points that can be used to build the GP model.Existence and construction of randomization defining contrast subspaces for regular factorial designs.
Thu, 09/03/2009 - 17:38 — SomNambulAuthors: Pritam Ranjan, Derek R. Bingham, Angela M. Dean
Subjects: Statistics
AbstractRegular factorial designs with randomization restrictions are widely used in
practice. This paper provides a unified approach to the construction of such
designs using randomization defining contrast subspaces for the representation
of randomization restrictions. We use finite projective geometry to determine
the existence of designs with the required structure and develop a systematic
approach for their construction. An attractive feature is that commonly used
factorial designs with randomization restrictions are special cases of this
general representation.
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