December 2022 Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model
Cheng Li
Author Affiliations +
Ann. Statist. 50(6): 3334-3363 (December 2022). DOI: 10.1214/22-AOS2230


Gaussian process models typically contain finite-dimensional parameters in the covariance function that need to be estimated from the data. We study the Bayesian fixed-domain asymptotics for the covariance parameters in a universal kriging model with an isotropic Matérn covariance function, which has many applications in spatial statistics. We show that when the dimension of domain is less than or equal to three, the joint posterior distribution of the microergodic parameter and the range parameter can be factored independently into the product of their marginal posteriors under fixed-domain asymptotics. The posterior of the microergodic parameter is asymptotically close in total variation distance to a normal distribution with shrinking variance, while the posterior distribution of the range parameter does not converge to any point mass distribution in general. Our theory allows an unbounded prior support for the range parameter and flexible designs of sampling points. We further study the asymptotic efficiency and convergence rates in posterior prediction for the Bayesian kriging predictor with covariance parameters randomly drawn from their posterior distribution. In the special case of one-dimensional Ornstein–Uhlenbeck process, we derive explicitly the limiting posterior of the range parameter and the posterior convergence rate for asymptotic efficiency in posterior prediction. We verify these asymptotic results in numerical experiments.

Funding Statement

The author was supported by the Singapore Ministry of Education Academic Research Funds Tier 1 Grants R-155-000-201-114 and A-0004822-00-00.


The author sincerely thanks the Associate Editor and two anonymous referees for valuable comments that have significantly improved the paper. The author thanks Michael L. Stein, Wei-Liem Loh, Wenxin Jiang, Sanvesh Srivastava and Yichen Zhu for helpful discussion.


Download Citation

Cheng Li. "Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model." Ann. Statist. 50 (6) 3334 - 3363, December 2022.


Received: 1 October 2021; Revised: 1 May 2022; Published: December 2022
First available in Project Euclid: 21 December 2022

MathSciNet: MR4524499
zbMATH: 07641128
Digital Object Identifier: 10.1214/22-AOS2230

Primary: 62E20 , 62F15 , 62H11

Keywords: asymptotic efficiency in posterior prediction , fixed-domain asymptotics , limiting posterior distribution , Matérn covariance function

Rights: Copyright © 2022 Institute of Mathematical Statistics


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Vol.50 • No. 6 • December 2022
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