Sequential, Bayesian geostatistics:a principled method for large data sets


The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Publication DOI:
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: The definitive version is available at Permissions sought by third parties must be acquired from Wiley-Blackwell
Uncontrolled Keywords: Gaussian random field models,geostatistics,predictive distribution,covariance estimation,sequential estimation algorithm,Geography, Planning and Development
Publication ISSN: 1538-4632
Last Modified: 02 Jan 2024 08:08
Date Deposited: 23 Nov 2010 15:29
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://onlineli ... 0635.x/abstract (Publisher URL)
PURE Output Type: Article
Published Date: 2005-04
Published Online Date: 2005-03-18
Authors: Cornford, Dan (ORCID Profile 0000-0001-8787-6758)
Csató, Lehel
Opper, Manfred



Version: Accepted Version

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