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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #330239
Title: Gaussian Process Regression with Large Data Sets: Has the Problem Been Solved?
Author(s): Sonja Surjanovic* and William Welch
Companies: University of British Columbia and University of British Columbia
Keywords: Design and analysis of computer experiments; Surrogate model; Gaussian process regression; Large-scale datasets

Computer models are used as surrogates for physical experiments in a large variety of applications. Nevertheless, the number of evaluations of the computer model is often severely limited due to the complexity and cost of the model. As a result, Gaussian process regression has proven to be a useful statistical emulator for complex computer experiments. However, even this statistical emulator can be very expensive for large designs, since the matrix calculations involved in evaluating the likelihood become intractable. We present an overview of the methods that have been proposed for overcoming this issue, and compare their performance in a variety of settings.

Authors who are presenting talks have a * after their name.

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