Gaussian process (GP) prediction suffers from O(n^3) scaling with the data set size n. By using a finite-dimensional basis to approximate the GP predictor, the computational complexity can be reduced. We derive optimal finite-dimensional predictors under a number of assumptions, and show the superiority of these predictors over the Projected Bayes Regression method (which is asymptotically optimal). We also show how to calculate the minimal model size for a given n. The calculations are backed up by numerical experiments.

Finite-dimensional approximation of Gaussian processes

FERRARI TRECATE, GIANCARLO;
1999-01-01

Abstract

Gaussian process (GP) prediction suffers from O(n^3) scaling with the data set size n. By using a finite-dimensional basis to approximate the GP predictor, the computational complexity can be reduced. We derive optimal finite-dimensional predictors under a number of assumptions, and show the superiority of these predictors over the Projected Bayes Regression method (which is asymptotically optimal). We also show how to calculate the minimal model size for a given n. The calculations are backed up by numerical experiments.
1999
9780262112451
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1486
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