Fast methods for training gaussian processes on large datasets

C. J. Moore*, A. J.K. Chua, C. P.L. Berry, J. R. Gair

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish (US)
Article number160125
JournalRoyal Society Open Science
Issue number5
StatePublished - May 11 2016


  • Data analysis
  • Gaussian processes
  • Inference
  • Regression

ASJC Scopus subject areas

  • General


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