Approximate Bayesian Inference for Geostatistical Generalised Linear Models

Research output: Contribution to journalArticle

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.
LanguageEnglish
JournalFoundations of Data Science
StatusAccepted/In press - 13 Feb 2019

Cite this

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title = "Approximate Bayesian Inference for Geostatistical Generalised Linear Models",
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 oftransformations of parameters, useful for practical applications. Issues regarding the choice of the parameters of the approximation such as the knots and taper range arealso addressed. Emphasis is given in applications in the context of disease mapping by illustrating the methodology for modelling the loa loa prevalence in Cameroon andmalaria in the Gambia.",
author = "Evangelos Evangelou",
year = "2019",
month = "2",
day = "13",
language = "English",
journal = "Foundations of Data Science",
issn = "2639-8001",
publisher = "American Institute of Mathematical Sciences",

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AB - 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 oftransformations of parameters, useful for practical applications. Issues regarding the choice of the parameters of the approximation such as the knots and taper range arealso addressed. Emphasis is given in applications in the context of disease mapping by illustrating the methodology for modelling the loa loa prevalence in Cameroon andmalaria in the Gambia.

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