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Local Gaussian Process Approximation for Large Computer Experiments

July 11, 2013 1:00PM to 2:00PM
Presenter 
Robert B. Gramacy, Assistant Professor, Booth School of Business, University of Chicago
Location 
University of Chicago, Searle Lab 240A
Type 
Meeting
Series 
Computational Institute Presentation
Information:
This talk will be broadcast to Argonne, TCS Building 240, Room 5172. You may also join the broadcast from your location by joining the Adobe Connect Meeting.

To join the meeting visit http://anl.adobeconnect.com/tcs-ci and enter as a guest.

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.