For many expensive computer simulators, the outputs are deterministic and thus the desired statistical surrogate (emulator) is an interpolator of the observed data. Gaussian spatial process (GP) is commonly used to model such simulator outputs. Fitting a GP model to n data points requires numerous inversion of a correlation matrix R. This becomes computationally unstable due to near-singularity of R. The popular approach to overcome near-singularity introduces over-smoothing of the data. In this talk, I will present an iterative regularization approach to construct a new predictor that gives higher prediction accuracy.
<a href="http://www.acadiau.ca/%7Epranjan/">Acadia University</a>
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