Approximate Bayesian Inference for Geostatistical Generalised Linear Models

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Abstract

The aim of this paper is to bring together recent developments in Bayesian generalised linear mixed models and geostatistics. We focus on approximate methods on both areas. A technique known as full-scale approximation, proposed by Sang and Huang (2012) for improving the computational drawbacks of large geostatistical data, is incorporated into the INLA methodology, used for approximate Bayesian inference. We also discuss how INLA can be used for approximating the posterior distribution of transformations of parameters, useful for practical applications. Issues regarding the choice of the parameters of the approximation such as the knots and taper range are also addressed. Emphasis is given in applications in the context of disease mapping by illustrating the methodology for modelling the loa loa prevalence in Cameroon and malaria in the Gambia.
Original languageEnglish
Pages (from-to)39-60
Number of pages22
JournalFoundations of Data Science
Volume1
Issue number1
DOIs
Publication statusPublished - 1 Mar 2019

Cite this

Approximate Bayesian Inference for Geostatistical Generalised Linear Models. / Evangelou, Evangelos.

In: Foundations of Data Science, Vol. 1, No. 1, 01.03.2019, p. 39-60.

Research output: Contribution to journalArticle

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