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Activity Number:
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169
- Advanced Bayesian Topics (Part 2)
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #318058
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Title:
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Fast Increased Fidelity Samplers for Approximate Bayesian Gaussian Process Regression
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Author(s):
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Kelly Moran* and Matthew Wheeler
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Companies:
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Los Alamos National Laboratory and National Institute of Environmental Health Sciences
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Keywords:
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Big data;
Bayesian Gaussian process;
Matrix compression;
Scalability;
Surface estimation;
Tensor product
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Abstract:
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Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H-matrix approximations that scale at O[n log^2(n)]. We show that this approximation's Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and real data sets.
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Authors who are presenting talks have a * after their name.
