Fast methods for training Gaussian processes on large datasets

Clicks: 222
ID: 272242
2016
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Abstract
Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large datasets. Here, we derive some simple results which we have found useful for speeding up the learning stage in the GPR algorithm, and especially for performing Bayesian model comparison between different covariance functions. We apply our techniques to both synthetic and real data and quantify the speed-up relative to using nested sampling to numerically evaluate model evidences.
Reference Key
gair2016royalfast Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Christopher Moore,A. J. K. Chua,Christopher Berry,J. R. Gair;Christopher Moore;A. J. K. Chua;Christopher Berry;J. R. Gair;
Journal Royal Society open science
Year 2016
DOI
10.1098/rsos.160125
URL
https://sciprofiles.com/publication/view/2172c2d1285599d54a39badcb2a2eebf
Keywords
statistics data analysis regression inference gaussian processes

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