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Bayesian inference in Gaussian model-based geostatistics.

Diggle, Peter J. and Ribeiro Jr, Paulo J. (2002) Bayesian inference in Gaussian model-based geostatistics. Geographical & Environmental Modelling, 6 (2). pp. 129-146. ISSN 1469-8323

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Abstract

In a geostatistical analysis, spatial interpolation or smoothing of the observed values is often carried out by a procedure known as kriging. In its basic form, kriging involves the construction of a linear predictor for an unobserved value of the process, and the form of this linear predictor is chosen with reference to the covariance structure of the data as estimated by a data-analytic tool known as the variogram. Often, no explicit underlying stochastic model is declared. We adopt a model-based approach to this class of problems, by which we mean that we start with an explicit stochastic model and derive associated methods of parameter estimation, interpolation and smoothing by the application of general statistical principles. In particular, we use Bayesian methods of inference so as to make proper allowance for the uncertainty associated with estimating the unknown values of model parameters. To illustrate the model-based approach we analyse data on precipitation levels in Paran State, Brazil.

Item Type: Article
Journal or Publication Title: Geographical & Environmental Modelling
Subjects: Q Science > QA Mathematics
Departments: Faculty of Health and Medicine > Medicine
VC's Office
ID Code: 19256
Deposited By: ep_ss_importer
Deposited On: 19 Nov 2008 11:36
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 15:27
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/19256

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