[Colloquium] Reminder: Seminar Announcement: Local Gaussian Process Approximation for Large Computer Experiments

[Ninfa Mayorga]

~Reminder

Computation Institute Presentation

Speaker: Robert B. Gramacy, Assistant Professor, Econometrics and Statistics, Booth School of Business, University of Chicago
Date: July 11, 2013
Time: 1:00 PM - 2:00 PM
Location: The University of Chicago, Searle 240A, 5735 S. Ellis Ave., This talk will be broadcast via Adobe Connect.

Local Gaussian Process Approximation for Large Computer Experiments

Abstract: We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential design schemes that dynamically define the support of a Gaussian process predictor based on a local subset of the data. We further derive expressions for fast sequential updating of all needed quantities as the local designs are built-up iteratively. Then we show how independent application of our local design strategy across the elements of a vast predictive grid facilitates a trivially parallel implementation. The end result is a global predictor able to take advantage of modern multicore architectures, while at the same time allowing for a nonstationary modeling feature as a bonus. We demonstrate our method on two examples utilizing designs sized in the thousands, and tens of thousands of data points. Comparisons are made to the method of compactly supported covariances. 

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