Spatial modeling with R‐INLA: A review

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee th...

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Bibliographic Details
Published in:	Wiley interdisciplinary reviews. Computational statistics Vol. 10; no. 6; pp. e1443 - n/a
Main Authors:	Bakka, Haakon, Rue, Håvard, Fuglstad, Geir‐Arne, Riebler, Andrea, Bolin, David, Illian, Janine, Krainski, Elias, Simpson, Daniel, Lindgren, Finn
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.11.2018 Wiley Subscription Services, Inc
Subjects:	analysis approximate Bayesian inference Bayesian analysis child-mortality cox processes Data Data analysis Data processing Economic models gaussian Gaussian Markov random fields Geostatistics Graphical methods Inference Isotropy Laplace approximations markov random-fields Matematik Mathematical models Mathematical sciences Modelling nested laplace approximation Partial differential equations plasmodium-falciparum point process models Probability theory soil properties sparse sparse matrices Spatial data spatial statistics Statistical analysis Statistical methods Statistical models stochastic partial differential equations Stochastic processes temporal
ISSN:	1939-5108, 1939-0068, 1939-0068
Online Access:	Get full text
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