Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy

Abstract

The stochastic partial differential equation approach to Gaussian processes (GPs) represents Matérn GP priors in terms of $n$ finite element basis functions and Gaussian coefficients with sparse precision matrix. Such representations enhance the scalability of GP regression and classification to datasets of large size $N$ by setting $n\approx N$ and exploiting sparsity. In this paper we reconsider the standard choice $n\approx N$ through an analysis of the estimation performance. Our theory implies that, under certain smoothness assumptions, one can reduce the computation and memory cost without hindering the estimation accuracy by setting $n\ll N$ in the large $N$ asymptotics. Numerical experiments illustrate the applicability of our theory and the effect of the prior lengthscale in the pre-asymptotic regime.

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• Rates of contraction of posterior distributions based on Gaussian process priors
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Cited by in corpus (1)

• The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running