Bayesian Inference for Multivariate Spatial Models with INLA

被引：0

作者：

Palmi-Perales, Francisco ^{[1
]}

Gomez-Rubio, Virgilio ^{[2
]}

Bivand, Roger S. ^{[3
]}

Cameletti, Michela ^{[4
]}

Rue, Havard ^{[5
]}

机构：

[1] Univ Valencia, Fac Math, Dept Stat & Operat Res, C Dr Moliner 50, Burjassot 46100, Spain

[2] Univ Castilla La Mancha, Dept Math, ETS Ingn Ind Albacete, La Mancha Ave Espana,S-n, Albacete 02071, Spain

[3] Norwegian Sch Econ, Dept Econ, Helleveien 30, N-5045 Bergen, Norway

[4] Univ Bergamo, Dept Econ, Via Caniana 2, IT-24127 Bergamo, Italy

[5] King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia

来源：

R JOURNAL | 2023年 / 15卷 / 03期

关键词：

POINT PATTERNS; GSTAT;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Bayesian methods and software for spatial data analysis are well-established now in the broader scientific community generally and in the spatial data analysis community specifically. Despite the wide application of spatial models, the analysis of multivariate spatial data using the integrated nested Laplace approximation through its R package (R-INLA) has not been widely described in the existing literature. Therefore, the main objective of this article is to demonstrate that R-INLA is a convenient toolbox to analyse different types of multivariate spatial datasets. This will be illustrated by analysing three datasets which are publicly available. Furthermore, the details and the R code of these analyses are provided to exemplify how to fit models to multivariate spatial datasets with R-INLA.

引用

页码：172 / 190

页数：19

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